Method for transmitting message in wireless communication system, and device therefor

Abstract

According to various embodiments, a device can: collect pieces of road environment data for at least one road section; receive, from a user equipment, a first message including movement state information and a first predicted movement path; and, on the basis of the first message and / or first road environment data for a first road section related to the user equipment among the pieces of road environment data, transmit, to the user equipment, a second message requesting data augmentation-based path prediction.

Description

Method for transmitting a message in a wireless communication system and device for the same

[0001] This relates to a method for devices and networks to transmit and receive messages in a wireless communication system and a device for doing so.

[0002] A wireless communication system is a multiple access system that supports communication with multiple users by sharing available system resources (e.g., bandwidth, transmission power, etc.). Examples of multiple access systems include CDMA (code division multiple access), FDMA (frequency division multiple access), TDMA (time division multiple access), OFDMA (orthogonal frequency division multiple access), SC-FDMA (single carrier frequency division multiple access), and MC-FDMA (multi carrier frequency division multiple access) systems.

[0003] Sidelink (SL) refers to a communication method in which User Equipment (UE) establishes a direct link to directly exchange voice or data between terminals without passing through a Base Station (BS). SL is being considered as a solution to address the burden on base stations caused by rapidly increasing data traffic.

[0004] V2X (vehicle-to-everything) refers to a communication technology that exchanges information with other vehicles, pedestrians, and infrastructure-equipped objects through wired or wireless communication. V2X can be classified into four types: V2V (vehicle-to-vehicle), V2I (vehicle-to-infrastructure), V2N (vehicle-to-network), and V2P (vehicle-to-pedestrian). V2X communication can be provided through PC5 interfaces and / or Uu interfaces.

[0005] Meanwhile, as more communication devices require larger communication capacities, the need for improved mobile broadband communication compared to existing Radio Access Technology (RAT) is emerging. Accordingly, communication systems considering services or terminals sensitive to reliability and latency are being discussed; next-generation radio access technology that incorporates improved mobile broadband communication, Massive Machine Type Communication (MTC), and Ultra-Reliable and Low Latency Communication (URLC) can be referred to as new radio access technology (new RAT) or new radio (NR). Vehicle-to-everything (V2X) communication can also be supported in NR.

[0006] Figure 1 is a diagram illustrating a comparison between V2X communication based on RAT prior to NR and V2X communication based on NR.

[0007] Regarding V2X communication, prior to NR, RATs mainly discussed methods for providing safety services based on V2X messages such as BSM (Basic Safety Message), CAM (Cooperative Awareness Message), and DENM (Decentralized Environmental Notification Message). V2X messages can include location information, dynamic information, attribute information, etc. For example, a terminal can transmit a CAM of the periodic message type and / or a DENM of the event-triggered message type to another terminal.

[0008] For example, the CAM may include basic vehicle information such as dynamic state information of the vehicle, such as direction and speed, static data of the vehicle, such as dimensions, external lighting conditions, and route history. For example, a terminal may broadcast the CAM, and the latency of the CAM may be less than 100ms. For example, in the event of an unexpected situation such as a vehicle breakdown or accident, the terminal may generate a DENM and transmit it to other terminals. For example, all vehicles within the transmission range of the terminal may receive the CAM and / or DENM. In this case, the DENM may have a higher priority than the CAM.

[0009] Since then, regarding V2X communication, various V2X scenarios have been presented in NR. For example, various V2X scenarios may include vehicle platooning, advanced driving, extended sensors, remote driving, etc.

[0010] For example, based on vehicle platooning, vehicles can dynamically form groups and move together. For example, to perform platoon operations based on vehicle platooning, vehicles belonging to said group can receive periodic data from the lead vehicle. For example, vehicles belonging to said group can use said periodic data to reduce or increase the distance between vehicles.

[0011] For example, based on enhanced driving, vehicles can be semi-automated or fully automated. For example, each vehicle can adjust trajectories or maneuvers based on data acquired from local sensors of nearby vehicles and / or nearby logical entities. Additionally, for example, each vehicle can mutually share driving intentions with nearby vehicles.

[0012] For example, based on extended sensors, raw data or processed data or live video data acquired through local sensors can be exchanged between vehicles, logical entities, pedestrian terminals and / or V2X application servers. Thus, for example, a vehicle can perceive an environment that is enhanced compared to the environment it can detect using its own sensors.

[0013] For example, based on remote driving, a remote driver or V2X application can operate or control a remote vehicle for a person unable to drive or for a remote vehicle located in a dangerous environment. For example, in cases where the route is predictable, such as in public transportation, cloud computing-based driving can be used for the operation or control of the remote vehicle. Additionally, access to a cloud-based back-end service platform, for example, can be considered for remote driving.

[0014] Meanwhile, methods to specify service requirements for various V2X scenarios, such as vehicle platooning, enhanced driving, extended sensors, and remote driving, are being discussed in NR-based V2X communication.

[0015] The technical problem that the present invention aims to solve is to provide a method for predicting or calculating the movement path of a terminal more accurately and efficiently.

[0016] The technical problems are not limited to those mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art to which the present invention belongs from the description below.

[0017] A method by a first device according to one aspect may include: collecting road environment data for at least one road section; receiving a first message from a terminal including movement status information and a first predicted movement path; and transmitting a second message to the terminal requesting a data augmentation-based path prediction based on at least one of the first road environment data for a first road section related to the terminal and the first message.

[0018] Alternatively, the data augmentation-based path prediction may predict the predicted movement path based on augmented data obtained by augmenting the first road environment data using a VAE (variational autoencoder) or a GAN (generative adversarial network).

[0019] Alternatively, the second message may be transmitted to the terminal based on the fact that the amount of data of the first road environment data is less than a specific threshold amount.

[0020] Alternatively, the first device may calculate a second predicted movement path for the terminal based on at least one of the first message, the first road environment data, and augmented data augmented by the first road environment data, and the second message may be transmitted to the terminal based on the difference between the first predicted movement path and the second predicted movement path being greater than a specific threshold.

[0021] Alternatively, the second message may be transmitted to the terminal based on the prediction that the terminal will enter a road section where the movement paths of a plurality of terminals intersect or merge.

[0022] Alternatively, the second message may be transmitted to the terminal based on the fact that the communication quality associated with the first road section is below a specific threshold quality.

[0023] Alternatively, the second message may include augmented data obtained by augmenting the first road environment data using a VAE (variational autoencoder) or a GAN (generative adversarial network).

[0024] Alternatively, the validity of the augmented data may be analyzed based on an authentication system separately configured for the virtual data.

[0025] Alternatively, the first road environment data may include at least one of accident occurrence location data, road structure data, traffic flow data, accident terminal trajectory data, accident cause data, accident type data, and weather data related to the first road section.

[0026] Alternatively, the second message may further include maneuver control information that controls the maneuver of the terminal.

[0027] According to another aspect, at least one non-transient computer-readable medium comprises instructions for performing operations when executed by at least one processor, said operations may include: collecting road environment data for at least one road section; receiving a first message from a terminal including movement status information and a first predicted movement path; and transmitting a second message to the terminal requesting a data augmentation-based path prediction based on at least one of the first road environment data for a first road section related to the terminal and the first message.

[0028] According to another aspect, the network comprises: a Radio Frequency (RF) transceiver; and a processor connected to the RF transceiver, wherein the processor controls the RF transceiver to collect road environment data for at least one road section, receives a first message from a terminal including movement status information and a first predicted movement path, and can transmit to the terminal a second message requesting a data augmentation-based path prediction based on at least one of the first road environment data for a first road section related to the terminal and the first message.

[0029] According to another aspect, a processing device controlling a first device comprises at least one processor; and at least one memory connected to the at least one processor and storing instructions that perform operations when executed by the at least one processor, wherein the operations may include collecting road environment data for at least one road section; receiving a first message from a terminal including movement status information and a first predicted movement path; and transmitting a second message to the terminal requesting a data augmentation-based path prediction based on at least one of the first road environment data for a first road section related to the terminal and the first message.

[0030] A method by a terminal according to another aspect may include: a step of calculating a first predicted movement path based on road environment data; a step of transmitting a first message including the first predicted movement path and movement status information of the terminal to a first device; and a step of calculating a second predicted movement path using augmented data for the road environment data and the road environment data based on receiving a second message from the first device that includes information requesting a path prediction based on data augmentation.

[0031] A terminal according to another aspect includes an RF (Radio Frequency) transceiver; and a processor connected to the RF transceiver, wherein the processor controls the RF transceiver to calculate a first predicted movement path for the terminal based on road environment data, transmits a first message including the first predicted movement path and movement status information of the terminal to a first device, and, upon receiving a second message from the first device that includes information requesting a path prediction based on data augmentation, calculates a second predicted movement path using augmentation data for the road environment data and the road environment data.

[0032] According to various embodiments, the movement path of a terminal can be predicted or calculated more accurately and efficiently in a wireless communication system. According to one example, the network can effectively minimize unnecessary computational resource usage and battery consumption by enabling a data augmentation-based path prediction operation to be performed at the terminal when the terminal's prediction performance is degraded.

[0033] The effects obtainable from various embodiments are not limited to those mentioned above, and other unmentioned effects will be clearly understood by those skilled in the art from the description below.

[0034] The drawings attached to this specification are intended to provide an understanding of the present invention, to illustrate various embodiments of the invention, and to explain the principles of the invention together with the description in the specification.

[0035] Figure 1 is a diagram illustrating a comparison between V2X communication based on RAT prior to NR and V2X communication based on NR.

[0036] Figure 2 shows the structure of an LTE system.

[0037] Figure 3 shows the structure of the NR system.

[0038] Figure 4 shows the structure of a wireless frame of NR.

[0039] Figure 5 shows the slot structure of an NR frame.

[0040] FIG. 6 shows a communication structure that can be provided in a 6G system according to one embodiment of the present disclosure.

[0041] FIG. 7 shows an electromagnetic spectrum according to one embodiment of the present disclosure.

[0042] FIG. 8 shows an example of a typical NTN scenario based on a transparent payload according to one embodiment of the present disclosure.

[0043] FIG. 9 shows an example of a typical NTN scenario based on a regenerative payload according to an embodiment of the present disclosure.

[0044] FIG. 10 shows an example of a sensing operation according to one embodiment of the present disclosure.

[0045] Figure 11 shows the radio protocol architecture for SL communication.

[0046] Figure 12 shows a terminal performing V2X or SL communication.

[0047] Figure 13 shows a resource unit for V2X or SL communication.

[0048] FIG. 14 shows an example of a BWP according to one embodiment of the present disclosure.

[0049] FIG. 15 illustrates a procedure in which a terminal performs V2X or SL communication according to a resource allocation mode, according to one embodiment of the present disclosure.

[0050] Figure 16 is a diagram illustrating a method for performing data augmentation using VAE.

[0051] FIG. 17 is a diagram illustrating a method for a first device to request a terminal to perform a data augmentation-based path prediction.

[0052] Figure 18 is a diagram illustrating how a terminal performs data augmentation-based path prediction.

[0053] FIG. 19 illustrates a communication system to which the present invention is applied.

[0054] FIG. 20 illustrates a wireless device that can be applied to the present invention.

[0055] FIG. 21 illustrates another example of a wireless device to which the present invention applies. The wireless device may be implemented in various forms depending on the use-example / service.

[0056] FIG. 22 illustrates a vehicle or autonomous vehicle to which the present invention is applied.

[0057] A wireless communication system is a multiple access system that supports communication with multiple users by sharing available system resources (e.g., bandwidth, transmission power, etc.). Examples of multiple access systems include CDMA (code division multiple access), FDMA (frequency division multiple access), TDMA (time division multiple access), OFDMA (orthogonal frequency division multiple access), SC-FDMA (single carrier frequency division multiple access), and MC-FDMA (multi carrier frequency division multiple access) systems.

[0058] Sidelink refers to a communication method in which User Equipment (UE) establishes a direct link to directly exchange voice or data between terminals without passing through a Base Station (BS). Sidelink is being considered as a solution to address the burden on base stations caused by rapidly increasing data traffic.

[0059] V2X (vehicle-to-everything) refers to a communication technology that exchanges information with other vehicles, pedestrians, and infrastructure-equipped objects through wired or wireless communication. V2X can be classified into four types: V2V (vehicle-to-vehicle), V2I (vehicle-to-infrastructure), V2N (vehicle-to-network), and V2P (vehicle-to-pedestrian). V2X communication can be provided through PC5 interfaces and / or Uu interfaces.

[0060] Meanwhile, as more communication devices require larger communication capacities, the need for improved mobile broadband communication compared to existing Radio Access Technology (RAT) is emerging. Accordingly, communication systems considering services or terminals sensitive to reliability and latency are being discussed; next-generation radio access technology that incorporates improved mobile broadband communication, Massive MTC, and URLLC (Ultra-Reliable and Low Latency Communication) can be referred to as new radio access technology (new RAT) or new radio (NR). Vehicle-to-everything (V2X) communication can also be supported in NR.

[0061] The following technologies can be used in various wireless communication systems such as CDMA (code division multiple access), FDMA (frequency division multiple access), TDMA (time division multiple access), OFDMA (orthogonal frequency division multiple access), and SC-FDMA (single carrier frequency division multiple access). CDMA can be implemented using wireless technologies such as UTRA (universal terrestrial radio access) or CDMA2000. TDMA can be implemented using wireless technologies such as GSM (global system for mobile communications), GPRS (general packet radio service), and EDGE (enhanced data rates for GSM evolution). OFDMA can be implemented using wireless technologies such as IEEE (institute of electrical and electronics engineers) 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802-20, and E-UTRA (evolved UTRA). IEEE 802.16m is an evolution of IEEE 802.16e and provides backward compatibility with systems based on IEEE 802.16e. UTRA is part of UMTS (universal mobile telecommunications system). 3GPP (3rd generation partnership project) LTE (long term evolution) is part of E-UMTS (evolved UMTS) which uses E-UTRA (evolved-UMTS terrestrial radio access), employing OFDMA in the downlink and SC-FDMA in the uplink.LTE-A (advanced) is an evolution of 3GPP LTE.

[0062] 5G NR is a successor technology to LTE-A and is a new clean-slate type mobile communication system with characteristics such as high performance, low latency, and high availability. 5G NR can utilize all available spectrum resources, ranging from low frequency bands below 1 GHz to mid-frequency bands from 1 GHz to 10 GHz, and high frequency (millimeter wave) bands above 24 GHz.

[0063] For clarity of explanation, the description focuses on LTE-A or 5G NR, but the technical concept of the embodiment(s) is not limited thereto.

[0064] Figure 2 shows the structure of an applicable LTE system. This can be called an E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network), or an LTE (Long Term Evolution) / LTE-A system.

[0065] Referring to FIG. 2, the E-UTRAN includes a base station (20; Base Station, BS) that provides a control plane and a user plane to a terminal (10). The terminal (10) may be fixed or mobile and may be referred to by other terms such as MS (Mobile Station), UT (User Terminal), SS (Subscriber Station), MT (Mobile Terminal), or Wireless Device. The base station (20) refers to a fixed station that communicates with the terminal (10) and may be referred to by other terms such as eNB (evolved-NodeB), BTS (Base Transceiver System), or Access Point.

[0066] Base stations (20) can be connected to each other through an X2 interface. The base station (20) is connected to the EPC (Evolved Packet Core, 30) through the S1 interface, more specifically to the MME (Mobility Management Entity) through the S1-MME and to the S-GW (Serving Gateway) through the S1-U.

[0067] The EPC (30) consists of an MME, an S-GW, and a P-GW (Packet Data Network-Gateway). The MME holds information regarding the terminal's connection information or capabilities, and this information is primarily used for managing the terminal's mobility. The S-GW is a gateway with an E-UTRAN as its endpoint, and the P-GW is a gateway with a PDN as its endpoint.

[0068] The layers of the Radio Interface Protocol between a terminal and a network can be classified into L1 (Layer 1), L2 (Layer 2), and L3 (Layer 3) based on the lower three layers of the Open System Interconnection (OSI) model, which is widely known in communication systems. Among these, the Physical Layer, belonging to Layer 1, provides Information Transfer Services using a physical channel, while the Radio Resource Control (RRC) layer, located at Layer 3, performs the role of controlling radio resources between the terminal and the network. To this end, the RRC layer exchanges RRC messages between the terminal and the base station.

[0069] Figure 3 shows the structure of the NR system.

[0070] Referring to FIG. 3, the NG-RAN may include gNBs and / or eNBs that provide user plane and control plane protocol termination to terminals. FIG. 7 illustrates a case where only gNBs are included. The gNBs and eNBs are connected to each other via Xn interfaces. The gNBs and eNBs are connected to the 5G Core Network (5GC) via NG interfaces. More specifically, they are connected to the access and mobility management function (AMF) via NG-C interfaces and to the user plane function (UPF) via NG-U interfaces.

[0071] Figure 4 shows the structure of a wireless frame of NR.

[0072] Referring to FIG. 4, radio frames can be used for uplink and downlink transmission in NR. The radio frame has a length of 10 ms and can be defined as two 5 ms half-frames (HF). A half-frame may contain five 1 ms subframes (SF). A subframe may be divided into one or more slots, and the number of slots within a subframe may be determined by the subcarrier spacing (SCS). Each slot may contain 12 or 14 OFDM(A) symbols according to the cyclic prefix (CP).

[0073] When normal CP is used, each slot may contain 14 symbols. When extended CP is used, each slot may contain 12 symbols. Here, the symbols may include OFDM symbols (or CP-OFDM symbols) and SC-FDMA (Single Carrier - FDMA) symbols (or DFT-s-OFDM (Discrete Fourier Transform-spread-OFDM) symbols).

[0074] Table 1 below shows the number of symbols per slot ((N) according to the SCS setting (u) when normal CP is used. slot symb ), number of slots per frame((N frame,u slot ) and the number of slots per subframe((N subframe,u slot ) exemplifies.

[0075] SCS (15*2 u )N slot symb N frame,u slot N subframe,u slot 15KHz (u=0)1410130KHz (u=1)1420260KHz (u=2)14404120KHz (u=3)14808240KHz (u=4)1416016

[0076] Table 2 shows the number of symbols per slot, the number of slots per frame, and the number of slots per subframe according to the SCS when an extended CP is used.

[0077] SCS (15*2 u )N slot symb N frame,u slot N subframe,u slot 60KHz (u=2)12404

[0078] In an NR system, the OFDM(A) numerology (e.g., SCS, CP length, etc.) can be configured differently among multiple cells that are merged into a single terminal. Accordingly, the (absolute time) interval of a time resource (e.g., subframe, slot, or TTI) (collectively referred to as TU (Time Unit) for convenience) composed of the same number of symbols can be configured differently among the merged cells.

[0079] In NR, multiple numerologies or SCSs may be supported to support various 5G services. For example, if the SCS is 15 kHz, a wide area in traditional cellular bands may be supported, and if the SCS is 30 kHz / 60 kHz, dense-urban, lower latency, and wider carrier bandwidth may be supported. If the SCS is 60 kHz or higher, a bandwidth greater than 24.25 GHz may be supported to overcome phase noise.

[0080] The NR frequency band can be defined by two types of frequency ranges. The two types of frequency ranges may be FR1 and FR2. The numerical values ​​of the frequency ranges may change, for example, as shown in Table 3 below. Among the frequency ranges used in an NR system, FR1 may mean "sub 6GHz range" and FR2 may mean "above 6GHz range" and may be referred to as millimeter wave (mmW).

[0081] Frequency Range designationCorresponding frequency rangeSubcarrier Spacing (SCS)FR1450MHz - 6000MHz15, 30, 60kHzFR224250MHz - 52600MHz60, 120, 240kHz

[0082] As described above, the numerical value of the frequency range of the NR system may change. For example, FR1 may include a band of 410 MHz to 7125 MHz as shown in Table 4 below. That is, FR1 may include a frequency band of 6 GHz (or 5850, 5900, 5925 MHz, etc.) or higher. For example, the frequency band of 6 GHz (or 5850, 5900, 5925 MHz, etc.) or higher included within FR1 may include an unlicensed band. The unlicensed band may be used for various purposes, for example, for communication for vehicles (e.g., autonomous driving).

[0083] Frequency Range designationCorresponding frequency rangeSubcarrier Spacing (SCS)FR1410MHz - 7125MHz15, 30, 60kHzFR224250MHz - 52600MHz60, 120, 240kHz

[0084] Figure 5 shows the slot structure of an NR frame.

[0085] Referring to FIG. 5, a slot contains multiple symbols in the time domain. For example, in the case of a normal CP, one slot may contain 14 symbols, but in the case of an extended CP, one slot may contain 12 symbols. Alternatively, in the case of a normal CP, one slot may contain 7 symbols, but in the case of an extended CP, one slot may contain 6 symbols.

[0086] A carrier includes multiple subcarriers in the frequency domain. A Resource Block (RB) can be defined as multiple (e.g., 12) consecutive subcarriers in the frequency domain. A Bandwidth Part (BWP) can be defined as multiple consecutive (P)RBs ((Physical) Resource Blocks) in the frequency domain and can correspond to a single numerology (e.g., SCS, CP length, etc.). A carrier can include up to N (e.g., 5) BWPs. Data communication can be performed through the active BWPs. Each element can be referred to as a Resource Element (RE) in a resource grid and can be mapped to a single complex symbol.

[0087] Meanwhile, a wireless interface between terminals or a wireless interface between a terminal and a network may be composed of L1, L2, and L3 layers. In various embodiments of the present disclosure, L1 layer may refer to the physical layer. Additionally, for example, L2 layer may refer to at least one of the MAC layer, RLC layer, PDCP layer, and SDAP layer. Additionally, for example, L3 layer may refer to the RRC layer.

[0088] FIG. 6 illustrates a communication structure that can be provided in a 6G system according to one embodiment of the present disclosure. The embodiment of FIG. 6 can be combined with various embodiments of the present disclosure.

[0089] New network characteristics in 6G may be as follows.

[0090] - Satellite Integrated Network

[0091] - Connected Intelligence: Unlike previous generations of wireless communication systems, 6G is innovative and will update wireless evolution from "connected things" to "connected intelligence." AI can be applied at each stage of the communication process (or at each step of the signal processing described below).

[0092] - Seamless integration of wireless information and energy transfer

[0093] - Ubiquitous Super 3D Connectivity: Connectivity to the network and core network functions of drones and very low Earth orbit satellites will create Super 3D connectivity in 6G ubiquitous.

[0094] Some general requirements regarding the new network characteristics of 6G mentioned above may be as follows.

[0095] - Small cell networks

[0096] - Ultra-dense heterogeneous network

[0097] - High-capacity backhaul

[0098] - Radar technology integrated with mobile technology: High-precision localization (or location-based services) through communication is one of the functions of 6G wireless communication systems. Therefore, radar systems will be integrated with 6G networks.

[0099] - Softwarization and virtualization

[0100] The core implementation technologies of the 6G system are described below.

[0101] - Artificial Intelligence: Introducing AI into communications can streamline and enhance real-time data transmission. AI can determine how complex target tasks are performed using numerous analyses. In other words, AI can increase efficiency and reduce processing latency. Time-consuming tasks such as handover, network selection, and resource scheduling can be performed instantly using AI. AI can also play a significant role in M2M, machine-to-human, and human-to-machine communication. Furthermore, AI can enable rapid communication in Brain-Computer Interfaces (BCI). AI-based communication systems can be supported by metamaterials, intelligent structures, intelligent networks, intelligent devices, intelligent cognitive radios, self-sustaining wireless networks, and machine learning.

[0102] - THz Communication: Data transmission rates can be increased by expanding bandwidth. This can be achieved by using sub-THz communication with wide bandwidth and applying advanced large-scale MIMO technology. THz waves, also known as sub-millimeter radiation, generally refer to a frequency band between 0.1 THz and 10 THz with corresponding wavelengths ranging from 0.03 mm to 3 mm. The 100 GHz-300 GHz band range (Sub-THz band) is considered the primary portion of the THz band for cellular communication. Adding the Sub-THz band to the mmWave band increases 6G cellular communication capacity. Among the defined THz bands, the 300 GHz-3 THz band is located in the far-infrared (IR) frequency band. Although the 300 GHz-3 THz band is part of the optical band, it lies at the boundary of the optical band and immediately following the RF band. Therefore, this 300 GHz-3 THz band exhibits similarities to RF.

[0103] FIG. 7 illustrates an electromagnetic spectrum according to one embodiment of the present disclosure. The embodiment of FIG. 7 may be combined with various embodiments of the present disclosure. Key characteristics of THz communication include (i) a widely available bandwidth to support very high data transmission rates, and (ii) high path loss occurring at high frequencies (highly directional antennas are indispensable). The narrow beam width generated by highly directional antennas reduces interference. The small wavelength of THz signals allows a much larger number of antenna elements to be integrated into devices and BSs operating in this band. This enables the use of advanced adaptive array techniques that can overcome range limitations.

[0104] - Large-scale MIMO technology

[0105] - Hologram beamforming (HBF)

[0106] - Optical wireless technology

[0107] - Free Space Optical Transmission Backhaul Network (FSO backhaul network)

[0108] - Quantum communication

[0109] - Cell-free communication

[0110] - Integration of wireless information and power transmission

[0111] - Integration of wireless communication and sensing

[0112] - Integrated access and backhaul network

[0113] - Big data analysis

[0114] - Reconfigurable intelligent metasurface

[0115] - Metaverse

[0116] - blockchain

[0117] - Unmanned Aerial Vehicle (UAV): UAVs or drones will be a critical element in 6G wireless communication. In most cases, high-speed data wireless connectivity can be provided using UAV technology. Base station (BS) entities can be installed on UAVs to provide cellular connectivity. UAVs can possess specific features not found in fixed BS infrastructure, such as easy deployment, robust line-of-sight links, and controlled degrees of freedom for mobility. During emergencies, such as natural disasters, the deployment of ground communication infrastructure is not economically feasible, and sometimes services cannot be provided in volatile environments. UAVs can easily handle these situations. UAVs will become a new paradigm in the field of wireless communication. This technology facilitates the three fundamental requirements of wireless networks: eMBB, URLLC, and mMTC. UAVs can also support various purposes, such as enhancing network connectivity, fire detection, disaster emergency services, security and surveillance, pollution monitoring, parking monitoring, and accident monitoring. Therefore, UAV technology is recognized as one of the most critical technologies for 6G communication.

[0118] - Autonomous Driving (Self-Driving): V2X (Vehicle to Everything), a core element in building autonomous driving infrastructure, refers to technologies that enable vehicles to communicate and share with various elements on the road for autonomous driving, such as wireless communication between vehicles (Vehicle to Vehicle, V2V) and between vehicles and infrastructure (Vehicle to Infrastructure, V2I). Fast transmission speeds and low-latency technologies are essential to maximize autonomous driving performance and ensure high safety. Furthermore, future autonomous driving may go beyond merely delivering warning or guidance messages to the driver to actively intervene in vehicle operation and directly control the vehicle in dangerous situations. Since the amount of information to be transmitted and received may become massive for this purpose, it is expected that 6G will be able to maximize autonomous driving through faster transmission speeds and lower latency compared to 5G.

[0119] - Non-terrestrial networks (NTN): An NTN may represent a network or network segment that uses radio frequency (RF) resources mounted on a satellite (or unmanned aerial system (UAS) platform). FIG. 8 illustrates an example of a typical NTN scenario based on a transparent payload according to one embodiment of the present disclosure. FIG. 9 illustrates an example of a typical NTN scenario based on a regenerative payload according to one embodiment of the present disclosure. The embodiment of FIG. 8 or FIG. 9 may be combined with various embodiments of the present disclosure. Referring to FIG. 8, the satellite (or UAS platform) may establish a service link with a UE. The satellite (or UAS platform) may be connected to a gateway via a feeder link. The satellite may be connected to a data network via the gateway. A beam footprint may refer to an area where signals transmitted by the satellite can be received. Referring to FIG. 9, a satellite (or UAS platform) can establish a service link with a UE. A satellite (or UAS platform) connected to a UE can be connected to another satellite (or UAS platform) via inter-satellite links (ISL). Another satellite (or UAS platform) can be connected to a gateway via a feeder link. Based on a replay payload, the satellite can be connected to a data network via another satellite and a gateway. If no ISL exists between the satellite and another satellite, a feeder link between the satellite and the gateway may be required. FIG. 8 and FIG. 9 are merely examples of NTN scenarios, and NTN can be implemented based on various scenarios.For example, a satellite (or UAS platform) may implement a transparent or regenerative (with on-board processing) payload. For example, a satellite (or UAS platform) may generate multiple beams across a designated service area depending on the satellite's (or UAS platform's) field of view. For example, the satellite's (or UAS platform's) field of view may vary depending on the on-board antenna diagram and the minimum elevation angle. For example, a transparent payload may include radio frequency filtering, frequency conversion, and amplification. Thus, the waveform signal repeated by the payload may not be altered. For example, a regenerative payload may include radio frequency filtering, frequency conversion and amplification, demodulation / decoding, switching and / or routing, and coding / modulation. For example, a regenerative payload may be substantially equivalent to carrying all or part of the base station functions on the satellite (or UAS platform).

[0120] - Integrated Sensing and Communication (ISAC): Radio sensing is a technology that uses radio frequencies to determine the instantaneous linear velocity, angle, and distance (range) of an object, thereby obtaining information about the characteristics of the environment and / or objects within the environment. Since radio frequency sensing capabilities do not require connecting to objects via devices within a network, they can provide services for object location determination without the need for devices. The ability to obtain range, velocity, and angle information from radio frequency signals can provide a wide range of new functions, such as various object detection, object recognition (e.g., vehicles, humans, animals, UAVs), and high-precision localization, tracking, and activity recognition. Radio sensing services can provide information to various industries (e.g., unmanned aerial vehicles, smart homes, V2X, factories, railways, public safety, etc.) that enable applications such as intruder detection, assisted vehicle steering and navigation, trajectory tracking, collision avoidance, traffic management, and health and traffic management. In some cases, radio sensing may utilize non-3GPP type sensors (e.g., radar, cameras) to further support 3GPP-based sensing. For example, the operation of a wireless sensing service, i.e., the sensing operation, may depend on the transmission, reflection, and scattering processing of wireless sensing signals. Thus, wireless sensing can provide an opportunity to enhance existing communication systems from communication networks to wireless communication and sensing networks. FIG. 10 illustrates an example of a sensing operation according to one embodiment of the present disclosure. The embodiment of FIG. 10 may be combined with various embodiments of the present disclosure. Specifically, FIG. 10 (a) illustrates an example of sensing using a sensing receiver and a sensing transmitter located at the same position (e.g., monostatic sensing), and FIG. 10 (b) illustrates an example of sensing using a separated sensing receiver and a sensing transmitter (e.g., bistatic sensing).

[0121] FIG. 11 illustrates a radio protocol architecture for SL communication. Specifically, FIG. 11 (a) shows the user plane protocol stack of NR, and FIG. 11 (b) shows the control plane protocol stack of NR.

[0122] The Sidelink Synchronization Signal (SLSS) and synchronization information are described below.

[0123] SLSS is an SL-specific sequence that may include PSSS (Primary Sidelink Synchronization Signal) and SSSS (Secondary Sidelink Synchronization Signal). The PSSS may be referred to as S-PSS (Sidelink Primary Synchronization Signal), and the SSSS may be referred to as S-SSS (Sidelink Secondary Synchronization Signal). For example, length-127 M-sequences may be used for S-PSS, and length-127 Gold sequences may be used for S-SSS. For example, a terminal may use S-PSS to detect a primary signal and obtain synchronization. For example, a terminal may use S-PSS and S-SSSS to obtain detailed synchronization and detect a synchronization signal ID.

[0124] PSBCH (Physical Sidelink Broadcast Channel) may be a (broadcast) channel through which basic (system) information that a terminal must know first is transmitted before transmitting or receiving SL signals. For example, the basic information may include information related to SLSS, Duplex Mode (DM), TDD UL / DL (Time Division Duplex Uplink / Downlink) configuration, information related to resource pools, types of applications related to SLSS, subframe offsets, broadcast information, etc. For example, to evaluate PSBCH performance, in NR V2X, the payload size of PSBCH may be 56 bits, including a 24-bit CRC.

[0125] S-PSS, S-SSS, and PSBCH may be included in a block format that supports periodic transmission (e.g., SL SS (Synchronization Signal) / PSBCH block, hereinafter S-SSB (Sidelink-Synchronization Signal Block)). The S-SSB may have the same numerology (i.e., SCS and CP lengths) as the PSCCH (Physical Sidelink Control Channel) / PSSCH (Physical Sidelink Shared Channel) within the carrier, and the transmission bandwidth may be within a (pre-)set SL BWP (Sidelink BWP). For example, the bandwidth of the S-SSB may be 11 RB (Resource Block). For example, the PSBCH may span 11 RB. Additionally, the frequency position of the S-SSB may be (pre-)set. Therefore, the terminal does not need to perform hypothesis detection at the frequency to discover the S-SSB in the carrier.

[0126] Meanwhile, in an NR SL system, multiple numerologies having different SCS and / or CP lengths may be supported. In this case, as the SCS increases, the length of the time resource for the transmitting terminal to transmit S-SSBs may decrease. Consequently, the coverage of S-SSBs may decrease. Therefore, to ensure S-SSB coverage, the transmitting terminal may transmit one or more S-SSBs to the receiving terminal within a single S-SSB transmission cycle according to the SCS. For example, the number of S-SSBs transmitted by the transmitting terminal to the receiving terminal within a single S-SSB transmission cycle may be pre-configured or configured for the transmitting terminal. For example, the S-SSB transmission cycle may be 160ms. For example, an S-SSB transmission cycle of 160ms may be supported for all SCSs.

[0127] For example, if the SCS is 15 kHz at FR1, the transmitting terminal may transmit one or two S-SSBs to the receiving terminal within one S-SSB transmission cycle. For example, if the SCS is 30 kHz at FR1, the transmitting terminal may transmit one or two S-SSBs to the receiving terminal within one S-SSB transmission cycle. For example, if the SCS is 60 kHz at FR1, the transmitting terminal may transmit one, two, or four S-SSBs to the receiving terminal within one S-SSB transmission cycle.

[0128] For example, if the SCS is 60 kHz at FR2, the transmitting terminal can transmit 1, 2, 4, 8, 16, or 32 S-SSBs to the receiving terminal within one S-SSB transmission cycle. For example, if the SCS is 120 kHz at FR2, the transmitting terminal can transmit 1, 2, 4, 8, 16, 32, or 64 S-SSBs to the receiving terminal within one S-SSB transmission cycle.

[0129] Meanwhile, when the SCS is 60 kHz, two types of CP may be supported. Additionally, depending on the CP type, the structure of the S-SSB transmitted by the transmitting terminal to the receiving terminal may differ. For example, the CP type may be Normal CP (NCP) or Extended CP (ECP). Specifically, for example, if the CP type is NCP, the number of symbols mapping PSBCH within the S-SSB transmitted by the transmitting terminal may be 9 or 8. On the other hand, for example, if the CP type is ECP, the number of symbols mapping PSBCH within the S-SSB transmitted by the transmitting terminal may be 7 or 6. For example, PSBCH may be mapped to the first symbol within the S-SSB transmitted by the transmitting terminal. For example, the receiving terminal receiving the S-SSB may perform Automatic Gain Control (AGC) operation during the first symbol interval of the S-SSB.

[0130] Figure 12 shows a terminal performing V2X or SL communication.

[0131] Referring to FIG. 12, in V2X or SL communication, the term terminal may primarily refer to a user's terminal. However, if network equipment such as a base station transmits and receives signals according to the communication method between terminals, the base station may also be considered a type of terminal. For example, terminal 1 may be a first device (100), and terminal 2 may be a second device (200).

[0132] For example, terminal 1 can select a resource unit corresponding to a specific resource within a resource pool, which represents a set of resources. Then, terminal 1 can transmit an SL signal using the said resource unit. For example, terminal 2, which is a receiving terminal, can be configured with a resource pool in which terminal 1 can transmit a signal, and can detect terminal 1's signal within said resource pool.

[0133] Here, if terminal 1 is within the connection range of the base station, the base station may inform terminal 1 of the resource pool. On the other hand, if terminal 1 is outside the connection range of the base station, another terminal may inform terminal 1 of the resource pool, or terminal 1 may use a pre-configured resource pool.

[0134] Generally, a resource pool can be composed of multiple resource units, and each terminal can select one or more resource units to use for its SL signal transmission.

[0135] Figure 13 shows a resource unit for V2X or SL communication.

[0136] Referring to FIG. 13, the total frequency resources of the resource pool can be divided into NF units, and the total time resources of the resource pool can be divided into NT units. Thus, a total of NF * NT resource units can be defined within the resource pool. FIG. 13 illustrates an example where the resource pool is repeated in a period of NT subframes.

[0137] As shown in FIG. 13, a single resource unit (e.g., Unit #0) may appear repeatedly over time. Alternatively, to obtain diversity effects in the time or frequency dimension, the index of the physical resource unit to which a single logical resource unit is mapped may change in a predetermined pattern over time. In this structure of resource units, a resource pool may refer to a set of resource units that a terminal intending to transmit an SL signal can use for transmission.

[0138] Resource pools can be subdivided into several types. For example, depending on the content of the SL signals transmitted from each resource pool, resource pools can be classified as follows.

[0139] (1) A Scheduling Assignment (SA) may be a signal containing information such as the location of the resource used by the transmitting terminal for transmission of the SL data channel, the Modulation and Coding Scheme (MCS) or Multiple Input Multiple Output (MIMO) transmission method required for demodulation of the data channel, and Timing Advance (TA). The SA may also be multiplexed and transmitted together with the SL data on the same resource unit, in which case the SA resource pool may refer to a resource pool in which the SA is multiplexed and transmitted together with the SL data. The SA may also be called the SL control channel.

[0140] (2) A Physical Sidelink Shared Channel (PSSCH) may be a resource pool used by a transmitting terminal to transmit user data. If SA is multiplexed and transmitted along with SL data on the same resource unit, only the form of the SL data channel excluding SA information can be transmitted from the resource pool for the SL data channel. In other words, REs (Resource Elements) that were used to transmit SA information on individual resource units within the SA resource pool can still be used to transmit SL data in the resource pool of the SL data channel. For example, the transmitting terminal can transmit by mapping the PSSCH to a succession of PRBs.

[0141] (3) The discovery channel may be a resource pool for a transmitting terminal to transmit information such as its ID. Through this, the transmitting terminal can enable adjacent terminals to discover it.

[0142] Even if the content of the SL signal described above is the same, different resource pools may be used depending on the transmission and reception attributes of the SL signal. For example, even if the same SL data channel or discovery message is used, it may be divided into different resource pools depending on the method of determining the transmission timing of the SL signal (e.g., whether it is transmitted at the time of reception of the synchronization reference signal or whether it is transmitted by applying a certain timing advance at the time of reception), the method of resource allocation (e.g., whether the base station assigns the transmission resource of an individual signal to the individual transmission terminal or whether the individual transmission terminal selects the individual signal transmission resource itself from within the resource pool), the signal format (e.g., the number of symbols occupied by each SL signal in one subframe, or the number of subframes used for the transmission of one SL signal), the signal strength from the base station, the transmission power strength of the SL terminal, etc.

[0143] FIG. 14 illustrates an example of a BWP according to an embodiment of the present disclosure. The embodiment of FIG. 14 may be combined with various embodiments of the present disclosure. In the embodiment of FIG. 14, it is assumed that there are three BWPs.

[0144] Referring to FIG. 14, the common resource block (CRB) may be a numbered carrier resource block extending from one end of the carrier band to the other. And, the PRB may be a numbered resource block within each BWP. Point A may indicate a common reference point for the resource block grid.

[0145] A BWP can be configured by point A, an offset from point A (NstartBWP), and a bandwidth (NsizeBWP). For example, point A may be an external reference point of the PRB of a carrier where the subcarrier 0 of all numerologies (e.g., all numerologies supported by the network on that carrier) is aligned. For example, the offset may be the PRB interval between the lowest subcarrier in a given numerology and point A. For example, the bandwidth may be the number of PRBs in a given numerology.

[0146] SLSS (Sidelink Synchronization Signal) is a sidelink-specific sequence and may include PSSS (Primary Sidelink Synchronization Signal) and SSSS (Secondary Sidelink Synchronization Signal). The PSSS may be referred to as S-PSS (Sidelink Primary Synchronization Signal), and the SSSS may be referred to as S-SSS (Sidelink Secondary Synchronization Signal). For example, length-127 M-sequences may be used for S-PSS, and length-127 Gold sequences may be used for S-SSS. For example, a terminal may use S-PSS to detect the initial signal and obtain synchronization. For example, a terminal may use S-PSS and S-SSSS to obtain detailed synchronization and detect the synchronization signal ID.

[0147] The PSBCH (Physical Sidelink Broadcast Channel) may be a (broadcast) channel through which basic (system) information that the terminal must know first is transmitted before transmitting or receiving SL signals. For example, the basic information may include information related to SLSS, Duplex Mode (DM), TDD UL / DL (Time Division Duplex Uplink / Downlink) configuration, information related to resource pools, types of applications related to SLSS, subframe offsets, broadcast information, etc. For example, to evaluate PSBCH performance, in NR V2X, the payload size of the PSBCH may be 56 bits, including a 24-bit CRC (Cyclic Redundancy Check).

[0148] S-PSS, S-SSS, and PSBCH may be included in a block format that supports periodic transmission (e.g., SL SS (Synchronization Signal) / PSBCH block, hereinafter S-SSB (Sidelink-Synchronization Signal Block)). The S-SSB may have the same numerology (i.e., SCS and CP lengths) as the PSCCH (Physical Sidelink Control Channel) / PSSCH (Physical Sidelink Shared Channel) within the carrier, and the transmission bandwidth may be within a (pre-)set SL BWP (Sidelink BWP). For example, the bandwidth of the S-SSB may be 11 RB (Resource Block). For example, the PSBCH may span 11 RB. Additionally, the frequency position of the S-SSB may be (pre-)set. Therefore, the terminal does not need to perform hypothesis detection at the frequency to discover the S-SSB in the carrier.

[0149] FIG. 15 illustrates a procedure in which a terminal performs V2X or SL communication according to a resource allocation mode, according to one embodiment of the present disclosure. The embodiment of FIG. 15 may be combined with various embodiments of the present disclosure.

[0150] Referring to FIG. 15(a), in resource allocation mode 1, the base station may schedule SL resources to be used by the terminal for SL transmission. For example, in step S1500, the base station may transmit information related to SL resources and / or information related to UL resources to the first terminal. For example, the UL resources may include PUCCH resources and / or PUSCH resources. For example, the UL resources may be resources for reporting SL HARQ feedback to the base station.

[0151] For example, the first terminal may receive information related to a dynamic grant (DG) resource and / or information related to a configured grant (CG) resource from the base station. For example, the CG resource may include a CG type 1 resource or a CG type 2 resource. In this specification, the DG resource may be a resource that the base station sets / assigns to the first terminal via downlink control information (DCI). In this specification, the CG resource may be a (periodic) resource that the base station sets / assigns to the first terminal via DCI and / or RRC messages. For example, in the case of a CG type 1 resource, the base station may transmit an RRC message containing information related to the CG resource to the first terminal. For example, in the case of a CG type 2 resource, the base station may transmit an RRC message containing information related to the CG resource to the first terminal, and the base station may transmit DCI related to the activation or release of the CG resource to the first terminal.

[0152] In step S1510, the first terminal may transmit a PSCCH (e.g., Sidelink Control Information or 1st-stage SCI) to the second terminal based on the resource scheduling. In step S1520, the first terminal may transmit a PSSCH (e.g., 2nd-stage SCI, MAC PDU, data, etc.) associated with the PSCCH to the second terminal. In step S1530, the first terminal may receive a PSFCH associated with the PSCCH / PSSCH from the second terminal. For example, HARQ feedback information (e.g., NACK information or ACK information) may be received from the second terminal via the PSFCH. In step S1540, the first terminal may transmit / report the HARQ feedback information to the base station via a PUCCH or PUSCH. For example, the HARQ feedback information reported to the base station may be information generated by the first terminal based on HARQ feedback information received from the second terminal. For example, the HARQ feedback information reported to the base station may be information generated by the first terminal based on a pre-set rule. For example, the DCI may be a DCI for scheduling SL.

[0153] Referring to FIG. 15(b), in resource allocation mode 2, the terminal can determine an SL transmission resource within an SL resource set by the base station / network or a preset SL resource. For example, the set SL resource or the preset SL resource may be a resource pool. For example, the terminal may autonomously select or schedule a resource for SL transmission. For example, the terminal may perform SL communication by selecting a resource itself within the set resource pool. For example, the terminal may select a resource itself within a selection window by performing a sensing and resource (re)selection procedure. For example, the sensing may be performed on a subchannel basis. For example, in step S1510, the first terminal, having selected a resource itself within the resource pool, may use the resource to transmit PSCCH (e.g., SCI (Sidelink Control Information) or 1st-stage SCI) to the second terminal. In step S1520, the first terminal can transmit PSSCH (e.g., 2nd-stage SCI, MAC PDU, data, etc.) associated with the PSCCH to the second terminal. In step S1530, the first terminal can receive PSFCH associated with the PSCCH / PSSCH from the second terminal.

[0154] Referring to FIG. 15 (a) or (b), for example, the first terminal may transmit an SCI to the second terminal over the PSCCH. Or, for example, the first terminal may transmit two consecutive SCIs (e.g., 2-stage SCIs) to the second terminal over the PSCCH and / or PSSCH. In this case, the second terminal may decode the two consecutive SCIs (e.g., 2-stage SCIs) to receive the PSSCH from the first terminal. In this specification, an SCI transmitted over the PSCCH may be referred to as the 1st SCI, the 1st SCI, the 1st-stage SCI, or the 1st-stage SCI format, and an SCI transmitted over the PSSCH may be referred to as the 2nd SCI, the 2nd SCI, the 2nd-stage SCI, or the 2nd-stage SCI format.

[0155] Referring to FIG. 15 (a) or (b), in step S1530, the first terminal can receive PSFCH. For example, the first terminal and the second terminal can determine a PSFCH resource, and the second terminal can use the PSFCH resource to transmit HARQ feedback to the first terminal.

[0156] Referring to FIG. 15(a), in step S1540, the first terminal can transmit SL HARQ feedback to the base station via PUCCH and / or PUSCH.

[0157] Meanwhile, the aforementioned sidelink may be defined as communication between terminals or direct communication between terminals. In this case, PSCCH may be defined as a physical control channel for communication between terminals, PSSCH as a physical data channel or physical sharing channel for communication between terminals, and PSFCH as a physical feedback transmission channel between terminals.

[0158] Meanwhile, the SoftV2X service or SoftV2X system is a system that utilizes V2X communication via a UU interface, wherein the SoftV2X server receives VRU messages or PSMs (Personal Safety Messages) from VRUs (Vulnerable Road Users) or V2X vehicles, transmits information about surrounding VRUs or vehicles based on the VRU messages or PSMs, analyzes road conditions where surrounding VRUs or vehicles are moving, and transmits messages notifying surrounding VRUs or vehicles of collision warnings based on the analyzed information. Here, the VRU message or PSM message is a message transmitted to the SoftV2X server via the UU interface and may include mobility information regarding the VRU, such as the VRU's location, direction of movement, movement path, and speed. In other words, the SoftV2X system receives mobility information of VRUs and / or vehicles related to V2X communication through the UU interface, and the SoftV2X server controls the driving paths and movement flow of VRUs, etc., based on the received mobility information via a network or similar means. Alternatively, the SoftV2X system may be configured in relation to V2N communication.

[0159] Meanwhile, various modeling developments and solutions incorporating AI / ML (Artificial Intelligence / Machine Learning) analysis using V2X data are being developed. For example, terminal trajectory data can be used to predict terminal movement paths or estimate risk levels between objects. Furthermore, the performance of analysis models can be improved by combining V2X data with other collectible traffic and road-related data (accidents, environment, traffic flow, geometry, etc.). Here, the information collected from V2X messages may include various information collected through sensor messages (Sensor Data Sharing Message, SDSM, Collective Perception Message, CPM, etc.) generated by various sensors / detectors, including video detectors, in addition to status messages (Basic Safety Message, BSM, Cooperative Awareness Message, CAM, Personal Safety Message, PSM, Vulnerable Road User Awareness Message, VAM, Probe Vehicle Data, PVD, etc.) and event messages (Road Side Information Message, RSM, Road Safety Alert, RSA, Decentralized Environmental Notification Message, DENM, etc.) directly transmitted from terminals and RSUs. To perform analysis based on such V2X data, as much V2X information or traffic-related information as possible may be required. However, there may be limitations in the collection of such V2X information or traffic-related information.For example, in actual road environments, the share of terminals equipped with V2X modules is not yet high, and even if V2X information for a specific section is collected using other sensors such as video detectors, a certain amount of training data needs to be accumulated to collect high-accuracy data for said specific section. In addition, since the raw data collected in this process may contain information related to personal privacy, it may be difficult to directly utilize the collected raw data due to personal privacy issues.

[0160] In particular, accident data required for modeling, such as accident risk analysis, is extremely scarce and can be very difficult to collect. For instance, in accident modeling heavily influenced by road and environmental factors within a road segment, collecting a certain level of accident data by type within a narrow spatial scope may be nearly impossible or time-consuming. Therefore, methods may be needed to address the limitations of collecting traffic-related data, including V2X data. Furthermore, when collecting local / raw data containing personal information, data collection methods that consider personal privacy issues may be required.

[0161] In the following, we propose a data augmentation method / device capable of effectively generating a larger amount of (virtual) data by combining multimodal data fusion technology and a generative model based on the characteristics of V2X data collected from various traffic-related information collection systems, including terminals, infrastructure, and sensors.

[0162] V2X Data and Multilayer Traffic Data Augmentation Method and Device

[0163] Recently, securing a sufficient amount of analytical data may be essential to effectively perform data preprocessing, AI / ML analysis, and simulations on infrastructure or on-devices with analysis processes.

[0164] If additional traffic-related information / data (or road environment data) can be secured by applying data augmentation techniques, the performance of analysis such as AI / ML analysis, simulation, and modeling in various traffic scenarios can be improved, and the learning speed and performance during the process of validating the domain characteristics of specific road sections can be enhanced. Furthermore, securing additional traffic-related information / data based on data augmentation can be utilized as an alternative to address personal privacy issues.

[0165] Traffic-related information / data (or road environment data) may include various types of data. For example, information collected in V2X messages as traffic-related information / data (or road environment data) may be information that can be obtained from status messages (BSM, CAM, PSM, VAM, PVD, etc.) and event messages (RSM, RSA, DENM, etc.) transmitted directly by terminals and RSUs (road side units), and sensor messages (SDSM, CPM, SBSM, etc.) generated by proxy from various sensors / detectors including image detectors. In addition, traffic-related information / data may also include additionally collectible traffic / road-related data (accidents, environment, traffic flow, geometry, etc.).

[0166] The system of the proposed device may include a data collection and preprocessing module, a data augmentation module, and a data verification module. The data collection and preprocessing module may perform tasks such as classification or categorization by data type / characteristics while collecting and preprocessing data, depending on the purpose of analysis. The data augmentation module may perform tasks such as generating virtual scenarios or type-specific virtual data and / or data transformation by selecting and applying appropriate analysis methodologies and algorithms to suit the purpose of utilizing or analyzing the augmented data. The data verification module may analyze the validity of the data through methods such as statistical analysis between the augmented data and actual data, and review by relevant experts. The system of the device described above in the proposed method may be used in individual terminals, local terminals / infrastructure (Edge), or a central server / center (Cloud / Digital Transformation (DT)), depending on the purpose. Meanwhile, the local terminals / infrastructure (Edge) and the central server / center (Cloud / Digital Transformation (DT)) may also be defined as networks.

[0167] Figure 16 is a diagram illustrating a method for performing data augmentation using VAE.

[0168] Various data augmentation methodologies have been developed in previous studies, and device systems can generate data by applying the methodology most suitable for the characteristics of the required data. As an example of a data augmentation methodology, a method combining data fusion technology and generative models can be proposed. A representative example is the Variational Autoencoder (VAE) methodology.

[0169] The VAE can be configured as follows.

[0170] - Encoder: Can map input data into a latent space and output it. For example, an encoder can receive input data x and output probability distribution parameters of a latent variable z (e.g., mean μ and variance σ).

[0171] - Sampler (or latent space): As a compressed representation of data, a vector (or latent vector) can be sampled from the latent space using the calculated probability distribution parameters.

[0172] - Decoder: Can convert a vector in the latent space into the original data space. For example, a decoder can generate data / output similar to the original data from an input latent vector.

[0173] The concept / procedure of VAE proposed in the proposed method is an example of a representative data augmentation methodology; however, various other methodologies can also be applied as the proposed method by considering the purpose of analysis / data augmentation, the nature of the data, the development / evolution of new methodologies, and performance aspects.

[0174] Specifically, the data augmentation device / method according to the proposed method can be used in various embodiments depending on the purpose, as described below. For instance, the data augmentation device / method can adaptively adjust the mechanism, such as selecting the subject of data augmentation and analysis or allowing multiple layers to partially cooperate, by considering various factors such as the scenario, purpose, traffic / environmental / geographical situation, and the state of each entity layer.

[0175] 1. Sparse Data Augmentation (Local / Center / Device)

[0176] In systems that receive and store online / offline data (or accident data), data with similar characteristics can be expanded or augmented using a small sample size when the amount of data required for analysis is insufficient. For example, to assess the accident risk of a specific road point or section, data related to conflict situations defining accidents or dangerous conditions approaching accidents may be essential. The characteristics of the information that may be included in this data (or accident data or road environment data) can be classified. For example, the above data (or accident data or road environment data) may include data classified into information related to the location of the accident (e.g., surrounding road geometry (e.g., highway, tunnel, general road, intersection, etc.), accident lane, etc.), surrounding traffic information (e.g., average traffic volume at the same time / period, average speed, etc.), accident terminal trajectory information (trajectory of the accident victim terminal, path of the accident perpetrator terminal, etc.), cause of the accident (e.g., inattention, failure to maintain a safe distance, drowsiness, drunk driving, secondary accident, etc.) and type of accident (e.g., rear-end collision, side collision, rollover, etc.), accident-related terminal type (e.g., vehicle-to-pedestrian, vehicle-to-vehicle, single-vehicle, motorcycle accident, etc.), weather information, etc. Such data (or accident data or road environment data) may have very different characteristics depending on spatial / temporal circumstances, and there may be limitations in collecting sufficient actual data for various types. Therefore, samples (or sample data) can be classified by case, and data can be augmented based on feature information extracted from the samples (or sample data). For example, the accident data augmentation procedure may consist of the steps of collecting and preprocessing existing accident data, classifying and defining accident types, applying data augmentation techniques, and verifying the augmented data. In the case of data augmentation techniques, analysis methodologies or algorithms suitable for the purpose may be applied.For example, in the case of sparse accident data of a minority class, methods such as applying a suitable SMOTE (Synthetic Minority Over-sampling Technique) algorithm and utilizing a GAN (Generative Adversarial Network) for the generation of virtual accident scenarios may be considered. Verification of augmented data may be performed through statistical similarity or through expert review in the relevant domain. In addition to accident data, sparse accident data (hereinafter referred to as sparse data), such as edge case data in various traffic scenarios, can be augmented or generated using similar procedures and methods.

[0177] Specific examples of the most representative accident modeling analysis utilizing sparse data may be as follows. First, the accident data required for analysis is defined, parameters with high utilization priority or importance are selected for each accident data set, and data augmentation of the entire accident data or data augmentation focused on high-importance parameters may be selectively performed. Specifically, the conditions for adaptively applying augmented data in accident modeling may be as follows. Meanwhile, the following operations may be performed by a network or a terminal.

[0178] (1) Conditions for the timing of augmentation (trigger for determining the need for augmentation)

[0179] A. Based on sparse scenario monitoring: Periodically evaluates the label / scenario distribution; if the sample size of a specific combination (e.g., rain + increased curvature + night + congestion, etc.) falls below a threshold, performs accident data augmentation (or, turns on the augmentation flag).

[0180] B. Performance Deviation Detection Basis: Performing accident data augmentation by targeting parameters matching subdomains where the error rate by subdomain (e.g., intersections / roundabouts / ramps / highways) exceeds a baseline (or threshold error rate).

[0181] C. Uncertainty-based: Perform additional augmented sample generation and retraining in sections with high predicted variance. For example, accident data for at least one road section among the road sections with high predicted variance (e.g., variance of the distribution of labels or scenarios) can be augmented.

[0182] D. Data Quality Degradation: In cases where observation gaps increase, such as due to rising V2X delays or drop rates, simulation-based augmentation focusing on communication and awareness parameters is applied. For example, accident data can be augmented in terms of V2X message transmission and reception parameters for at least one road section where the V2X message transmission and reception delay and / or V2X message transmission and reception drop rate exceed a specific threshold.

[0183] (2) Augmentation method (target augmentation policy)

[0184] A. Maintaining Covariate / Causal Consistency: Augmenting accident data while ensuring physical consistency by co-sampling associated parameters (e.g., speed-braking distance-friction coefficient, curvature-claution-yaw, etc.)

[0185] B. Compliance with Physical / Regulatory Boundaries: Augmentation of accident data under constraints of the realism range specified in the table (e.g., braking limits, lane width, signal operation rules, etc.)

[0186] C. Utility-weighted sampling: Automatically adjusts the augmentation of incident data and / or the learning rate by applying weights based on scenario importance to the loss function.

[0187] (3) Learning loop: synthetic-to-real ratio adjustment

[0188] A. Initial Interval: When data for the regression combination is non-existent, bootstrap with the default value (e.g., synthetic 70%)

[0189] B. Upon accumulation of field data: Gradual attenuation of the augmented data ratio in accordance with the ratio of actual accident data secured

[0190] C. Mini-batch mixing within an epoch: Mixing actual and augmented accident data in the same mini-batch, deriving domain-invariant features, and selecting methodologies

[0191] (4) Evaluation of augmented accident data: Decision on adoption

[0192] A. Safety Threshold Check: Rollback or re-enhancement if underestimated risk in the severity model exceeds the threshold

[0193] B. Calibration Correction: Reliability correction of the probabilistic model after augmentation

[0194] (5) Examples of key parameters by data (Augmentation target)

[0195] Key parameters for each data type related to accident data can be defined as shown in Table 5 below.

[0196] Data Type Parameters Accident Impact / Evidence Major Sources Vehicle Data Speed, acceleration, deceleration, etc. Direct correlation with collision probability / severity: CAM / BSM / CAN / IMU, etc. Heading / Yaw, steering angle, etc. Direct correlation with lane departure / side collision: CAM / BSM / IMU, etc. Distance between vehicles / relative speed: TTC, collision probability Key Input Radar / Camera, CPM / SDSM / BSM, etc. Traffic Data Density, average speed, etc. Correlation with collective risk: Rear-end collision / tailgating, etc.: Loop / camera detector, center information, etc. Signal cycle / offset, etc. Determination of stop-start, collision patterns: SPAT, operational DB, etc. Mixed vehicle ratio Visibility, braking distance, interaction differential: Loop / camera detector, center information, etc. Right / left turn proportion, U-turn frequency, etc. Increase in conflict points: Lane-specific detection, MAP, etc. Geometric Data Number of lanes / width / curvature / longitudinal slope, etc. Visibility, braking distance, lane keeping difficulty, etc.: MAP, HD map, etc. Intersection shape Conflict points, path Complexity (MAP, etc.) Road surface conditions (Degradation of braking and steering safety, RWM / TIM / PVD, road surface sensors, etc.) Environmental data (Weather - precipitation, visibility, etc. - Direct impact on accident frequency / severity, Weather API / RSU, RWM / TIM / PVD, etc.) Time (peak / night / weekend) (Exposure and congestion changes, Center information, etc.) Road surface temperature / freezing, etc. (Changes in friction and braking distance, RWM / TIM / PVD, road surface sensors, etc.) Perception / communication performance data (V2X latency / loss rate, etc.) Assessment of cooperative perception / warning efficacy, RSU / TCU QoS, etc. Affects timing for avoiding sensor recognition errors / latency, Sensor logs, etc. Risk of position error judgment errors, GNSS, GPS logs, etc.

[0197] 2. Thought Modeling

[0198] Through the aforementioned conditions and augmentation process, appropriate accident data deemed necessary for accident modeling can be augmented. The process of performing accident modeling using such augmented accident data may be as follows. Meanwhile, as described above, road environment data may include the aforementioned accident data, and not only accident data but also various types of data (e.g., surrounding environment data that may influence the prediction / determination of the terminal's movement path) may be augmented by the method described below.

[0199] (1) Setting goals for thought modeling

[0200] The goals of accident modeling may include accident frequency, severity, and probability for a specific road section. Here, accident frequency can be a prediction of how often accidents occur under specific temporal and spatial conditions. Accident severity can be a prediction of the level of damage (e.g., minor injury, serious injury, or death) when an accident occurs. Accident probability can be a prediction of the probability of an accident occurring given specific conditions.

[0201] Alternatively, as described above, the purpose may be to generate augmented data to reflect various scenarios that can affect the prediction of the terminal's movement path in the specific road section, and interactions with surrounding devices / terminals / vehicles.

[0202] (2) Data Source Integration: Define / configure the data sources required for the configuration goals set in '(1)', and integrate the defined / configured data sources.

[0203] (3) Feature selection: Key parameters / variables with high accident influence can be prioritized in the analysis conditions of each data source.

[0204] (4) Check for missing / sparse data: Check whether the amount of data from a data source required for the setting goal set in '(1)' is less than or equal to a specific threshold amount of data (e.g., check for a lack of data for accident modeling). If the amount of data from a data source is less than or equal to the specific threshold amount of data, data augmentation related to the data source may be performed.

[0205] (5) Augmentation of target / data source

[0206] When data augmentation related to the above data source is performed, the deficient conditions / variables can be artificially created in compliance with the physical / regulatory constraints described in “1. (2) Augmentation method (targeted augmentation policy) of sparse data augmentation.”

[0207] (6) Model training for accident modeling

[0208] Models for accident modeling may be as follows, and the following models can be trained using the aforementioned augmented data / data sources.

[0209] A. Frequency Models: Poisson regression, time series forecasting, spatial statistical models, etc. (e.g., forecasting the number of accidents by intersection and time period, etc.)

[0210] B. Probabilistic Models: Logistic Regression, Random Forest, XGBoost, Neural Networks, AI, etc. (e.g., estimation of collision probability under the condition TTC < 1.5 seconds)

[0211] C. Severity Models: Multinomial Logistics, Survival Analysis, GAMLSS, etc. (e.g., estimation of probability distributions for minor / serious injury / death based on relative velocity values, etc.)

[0212] (7) Validation

[0213] The validity of the trained models can be verified based on the following indicators.

[0214] A. General indicators: AUC (Area Under the Curve), Precision-Recall, RMSE (Root Mean Square Error)

[0215] B. Detailed Metrics: Performance by Specific Scenario (Night / Rain / Curved Sections, etc.)

[0216] C. Calibration: Verify whether the predicted probability / value matches the actual value.

[0217] (8) Operation / Monitoring

[0218] The method for operating / monitoring trained models may be as follows.

[0219] A. Real-time Application: The terminal or / and RSU or / and server recognizes periods of increased accident probability and provides warnings.

[0220] B. Periodic Retraining: When data distribution changes (e.g., vehicle type ratio (including autonomous vehicles), weather / seasonal characteristics, presence of construction, etc.), perform data augmentation and retraining of the accident modeling model using the augmented data.

[0221] In the following, a method for augmenting road environment data to predict a terminal's path or movement path based on the data augmentation methods described above, and / or conditions under which an operation to predict a terminal's movement path by additionally reflecting the augmented road environment data (or augmented data) is performed are explained in detail.

[0222] 3. Terminal path prediction (Terminal / Local / Center)

[0223] Predicting the future trajectory or future movement path of a terminal is a very difficult task. In a free flow state where there are no terminals or obstacles nearby, it is sufficient to predict only behavioral changes in the longitudinal and / or lateral directions for individual terminals; however, in a real road environment, it is difficult to accurately predict the movement path of a terminal based solely on such simple predictions. For example, even if the subject terminal (or the subject vehicle's terminal) does not wish to change its behavior, unavoidable behavioral changes may occur due to the influence or interaction of changes in the speed, acceleration, yaw, etc., of surrounding terminals or vehicles. Furthermore, the occurrence of unexpected obstacles, changes in geometry (e.g., mergers, curves, uphill / downhill slopes, etc.), and weather changes can also affect changes in the driving behavior or driving pattern of the subject terminal (various interactions occur). It may be practically impossible to acquire all information or data regarding these changes and interaction relationships in the driving behavior or driving pattern of the subject terminal (e.g., road environment data for various scenarios). Therefore, it is necessary to improve the performance of the terminal's path prediction through VAE-based path prediction, simulation using augmented data samples, or AI / ML analysis. In this case, in addition to methods for performing on-device analysis based on information collected by the terminal itself and received V2X information, if a local infrastructure (Edge) such as an RSU or a central server (cloud) determines that the terminal's prediction performance / capability of the predicted movement path has deteriorated based on the information collected from the terminal, it may trigger the execution of an operation to predict a predicted movement path that additionally considers the augmented data, or provide the augmented data to the terminal.Alternatively, if a local infrastructure (Edge) such as an RSU or a central server (cloud) determines that the prediction performance / capability of the terminal's predicted movement path has deteriorated based on information collected about the terminal, it may surrogately analyze the terminal's future path / predicted movement path and provide information regarding the analyzed future path / predicted movement path to the terminal.

[0224] The generation of such augmented data may be considered in the following manner. A network (or local server and / or terminal) may generate / acquire augmented data by augmenting the road environment data using an augmentation model (VAE or GAN) based on an artificial neural network, so as to reflect various driving conditions and interaction scenarios of surrounding vehicles that are difficult to obtain using only actual road environment data. For example, the network (or local server and / or terminal) may generate augmented data, which is virtual road environment data for various environmental scenarios, by changing various environmental conditions based on road environment data (e.g., collected data regarding the actual road environment) for a specific road section related to the terminal. For example, the network may acquire / generate the augmented data by varying physical characteristics such as speed, acceleration, yaw, braking patterns, and lane change frequency of surrounding terminals or vehicles traveling on the road section related to the terminal, as well as environmental factors such as road congestion, vehicle density, and communication signal delay. In this case, the terminal can predict its predicted movement path more precisely and flexibly even in complex driving scenarios by additionally utilizing the augmented data to calculate and predict its predicted movement path. Furthermore, the terminal can significantly improve the accuracy of its predicted movement path by predicting the movement path using augmented data configured to simulate various abnormal situations or unexpected variables that may occur during driving.

[0225] Next, as an example of one of the various analysis methods based on the aforementioned augmented data, the VAE-based path prediction procedure and principle are as follows.

[0226] (i) Data compression and reconstruction: VAE compresses high-dimensional path data into a low-dimensional latent space and reconstructs it back into the original space.

[0227] (ii) Probabilistic Modeling: VAE models path uncertainty as the probability distribution of latent variables.

[0228] (iii) Conditional generation: VAE generates future paths / predicted travel paths using past trajectories and road environment information as conditions.

[0229] In the process described above, methods to improve the prediction performance of the predicted travel path may include a multi-scale approach (Macro / Micro stage), environment recognition (combining environment maps and trajectory data), conditional prediction of driving habits, interaction modeling, historical trajectory estimation, various sampling, and simulation using augmented data for each traffic scenario. Additionally, the accuracy of the prediction of the predicted travel path or the prediction model that predicts the travel path (e.g., the VAE-based prediction model described above) can be improved based on a loss function that reconstructs the difference between the predicted travel path and the actual travel path (or the predicted trajectory and the actual trajectory). In this way, the VAE-based prediction model can predict the predicted travel path of the terminal more accurately and diversely through learning that considers not only the complex patterns of the path data but also the characteristics of the environment and the individual.

[0230] Based on the analysis methodology derived from the aforementioned data augmentation method, it is also possible to provide the infrastructure / server / network with characteristic / environmental information for each road section according to the location of the terminal, analysis information based on the characteristic / environmental information for each road section, or data that can be referenced during analysis at the terminal (sufficient historical information or data augmented based on historical information). For example, if augmented data is required for the prediction of a predicted movement path at the terminal, the infrastructure / server / network may provide the terminal with traffic data related to the road section, augmented data augmented based on said traffic data, and / or analysis data analyzed based on the terminal's mobility information and augmented data.

[0231] Specifically, under certain conditions, the terminal and the network (or local server / center) can improve the prediction / analysis performance of the terminal's predicted movement path through a method of exchanging augmented data with each other.

[0232] In the proposed method, the agents and operations can operate on various entities (e.g., terminals, local systems, servers) or be performed through a combination of various entities. Through this, the quality of analysis information can be improved by providing additional information (e.g., road environment data or augmented data) necessary for predicting the predicted movement path at the terminal. Furthermore, direct intervention and / or support, such as coordinating the terminal's maneuvers within the network, may be possible. Signaling for the request or exchange of necessary information between entities may be required based on various conditions, such as the current state of the terminal, the level of information known about the terminal, and analysis performance. Examples of the roles and signaling methods of each entity (e.g., messages, trigger points, etc.) may be as follows. Meanwhile, although the proposed invention is described primarily focusing on the roles and signaling methods of each entity based on the current technical level, the operations described below may be operated in combination depending on the performance of the terminal resulting from technological advancements.

[0233] (1) Entity (or component) Role and Characteristics

[0234] 1) Terminal: i) Equipped with real-time sensors, vehicle status, and internal path prediction modules, ii) Prioritizes operation (low latency) when fast local decisions are required

[0235] 2) Local Server: i) Fusion of information from multiple nearby terminals and infrastructure, ii) Prediction from a broader perspective than the terminal, generation of collaborative recommendations, low / medium latency

[0236] 3) Server: i) Holding / storing large-scale data, ii) Complex training / retraining, model updates, policy decisions, high latency

[0237] (2) Signaling (Message) - Key Field

[0238] The request or exchange of road environment data or augmented data can be signaled through existing / transformed fields included in existing V2X messages, newly additionally defined fields for existing V2X messages, and / or newly defined messages (newly standardized messages). Through such a signaling method, the terminal can perform augmented data generation, augmented data reception, and / or analysis data reception based on its own judgment, information received from the local / server, and / or instructions requested / recommended by the local / server.

[0239] 1) STATUS_UPDATE (Terminal → Local / Server): The terminal can provide / update its state / mobility information to the local / server / network. For example, the terminal can provide / update its state / mobility information / predicted movement path information by sending BSM, CAM, PSM and / or VAM to the local / server / network.

[0240] 2) ENV_AGG (Local → Terminal / Server): The local / server can provide the terminal with information regarding timestamp, rsu_id, detected object(list), aggregated_traffic_density, SPaT_phase, MAP, etc. (e.g., road environment data).

[0241] 3) ASSISTANCE ADVICE (Local / Server → Terminal): The local / server may provide the terminal with information / instructions regarding advice_types (e.g., info, warning, advisory, action_request, etc.), geometry, recommended_speed, recommended_start, validity_window, rationale, etc.

[0242] 4) maneuver_REQUEST / MANEVER_CONFIRM (Terminal ↔ Local): The terminal and the local / server can exchange messages containing information / fields such as request_id, target_maneuver, required_authority_level, safety_check_flag, accept / reject, execution_metrics, etc.

[0243] 5) MODEL_UPDATE (Server / Local → Terminal (and / or Local)): For example, the server / local may provide the terminal with information for updating a path prediction model that predicts the predicted path described above (e.g., parameter values ​​of the prediction path model).

[0244] (3) Trigger point example

[0245] Data augmentation and information exchange may be performed for each entity (or component) when a specific trigger type or criterion is satisfied. Specific trigger types (or trigger conditions / criteria) may be as shown in Table 6 below. Meanwhile, the trigger conditions / trigger criteria presented in Table 6 may be flexibly added, deleted, or modified depending on the situation of the terminal, and the terminal / server / network may determine whether to perform the aforementioned data augmentation and / or information exchange operation based on at least one of the trigger conditions / trigger criteria presented in Table 6.

[0246] Entity Trigger Criteria Examples Measurement / Indicator Examples Decreased terminal location accuracy (GPS, GNSS, etc.) Increased prediction uncertainty (Grade of difference between local and server analysis results in prediction / analysis values ​​(e.g., route prediction reliability)) Severe state changes (Velocity, acceleration, Yaw isotopes / sensor anomalies) Sensor accuracy, reliability, etc. Resource / Battery shortage (CPU usage, battery level, etc.) Local traffic collision potential (Multiple terminal route intersection, aggregated accident risk, etc.) Infrastructure anomaly detection (Sudden SPaT changes, lane closures, etc.) Server long-term statistical anomaly detection (Surge in accident frequency in the same section) Degradation of common communication quality (Packet loss, late, latency, etc.)

[0247] (4) Intervention level

[0248] Meanwhile, the local / server (e.g., network) not only performs actions such as providing augmented data and triggering path prediction operations based on augmented data for the terminal, but may also provide driving control information of the terminal to the terminal as needed (e.g., depending on the intervention level), as described below.

[0249] Specifically, the server / network defines an intervention level related to the terminal and can take stepwise measures based on the intervention level, and the intervention level and measures may be integrated / combined / changed / deleted.

[0250] 1) Intervention Level 0 - Passive info (Information provision)

[0251] At intervention level 0, the server / network can perform actions such as UI warnings and local log storage for the purpose of assisting driver / system awareness related to the terminal.

[0252] 2) Intervention Level 1 - Advisory

[0253] At intervention level 1, the server / network can perform actions such as driver HMI recommendations and providing ACC recommended speeds for the purpose of safety improvement recommendations related to the terminal (e.g., deceleration / distance maintenance).

[0254] 3) Intervention Level 2 - Recommended Activation (Information Provision)

[0255] At intervention level 2, the server / network can perform actions such as lane change recommendation notification / display for the purpose of specific operation recommendations related to the terminal (e.g., lane change deferral, detour, etc.), and request control as a recommendation in automatic driving mode.

[0256] 4) Intervention Level 3 - Action request (Information provision)

[0257] At intervention level 3, the server / network may perform actions such as requesting emergency control to the terminal and executing upon passing terminal safety verification for the purpose of active intervention requests, such as collision avoidance related to the terminal.

[0258] 5) Intervention Level 4 - Forced Control (Information Provision)

[0259] At intervention level 4, the server / network can perform forced execution of terminal automatic control in cases where avoidance of human casualties or large-scale accidents is evident.

[0260] 5. Generate training data (Local / Center / Server / Network)

[0261] When installing new video detection-based road infrastructure or edges, traffic, road, and environmental characteristics can vary significantly across different road sections. Therefore, to improve performance aspects such as the success rate of video detection, a calibration and verification phase involving the collection and learning of sufficient traffic information for the newly installed road sections may be essential. Such sufficient data collection and learning / verification may require a long period of time. In this case, initial training and simulation can be performed using a small amount of data collected over a short period for each pattern (or traffic pattern), utilizing modified, replicated, or augmented data from various scenarios. This approach allows for the infrastructure's training to be processed efficiently within a short timeframe.

[0262] 6. Personal Privacy Protection Measures (Terminal / Local)

[0263] Video data collected from terminals such as RSUs, video detectors, and black boxes, or from local infrastructure, may not be processed immediately at the network / edge. This is because the collected video data may contain personally identifiable information of others, and transmitting such data to a server, center, or cloud could lead to issues regarding the infringement of personal privacy. Furthermore, regulatory issues arising from personal information protection laws may occur. Moreover, users of individual terminals may not wish to share their personal privacy information, such as personally identifiable information and location data. Therefore, it is necessary to significantly mitigate the aforementioned personal privacy issues by utilizing data augmentation techniques. In particular, since personal privacy issues may arise when video data is transmitted or shared without the individual's consent, methods to modify the video data (e.g., images) through techniques such as rotation, flipping, or cropping may be performed or applied first. For instance, in the case of path data, methods such as adding noise or sampling may be considered. Although V2X messages (including those generated based on information extracted from video) are encrypted, privacy issues may arise when messages are generated based on information about other objects. As an alternative to protect against such infringement of personal privacy, the following methods may be utilized.

[0264] - 1) Deletion / De-identification / Conversion of Personally Identifiable Information

[0265] - 2) Data Augmentation (e.g., GAN): Generating / sharing similar virtual data by learning the characteristics of real data

[0266] - 3) Feature Vector Extraction: Feature vectors of the original data (e.g., probability distribution parameters in the case of VAE) are extracted from the data collection system (individual terminals or local infrastructure) and shared, and similar data is generated in the cloud based on this information.

[0267] - 4) Differential privacy: Adding statistical noise to the dataset partially or gradually

[0268] - 5) Federation learning: A model is trained within a data collection system (individual terminal or local infrastructure), and only the parameters necessary for modeling are shared. For example, a network or local infrastructure can train a prediction model for risk detection based on collected traffic data and provide a set of parameters for the trained prediction model to the terminal.

[0269] 7. Data Feature Vector Archiving (Terminal / Local / Central)

[0270] Terminals generally have lower storage capacity compared to infrastructure. In such cases, information collected or received by the terminal itself may be treated as data that is no longer valid or necessary after a certain period, and the terminal may discard, remove, or delete such collected data. In particular, issues may arise when terminal information containing personal information is stored for extended periods. Infrastructure such as local or central facilities has higher storage capacity compared to terminals; however, the volume of data collected can be massive due to the collection of data and information from multiple devices and diverse environments. Consequently, while infrastructure such as local or central facilities may have a longer storage period for collected data compared to terminals, they may not continuously collect or store all data. As such, when data accumulates continuously regardless of the data collector, issues regarding device storage space and capacity may arise. To address this problem, a more efficient data archiving approach can be considered by applying data augmentation techniques. For example, data / information collected and received by information collection systems (vehicles, VRUs, and / or RSUs, etc.) such as terminals existing in a specific road section can be transmitted / delivered to a local / center server. The local / center server cannot store all collected data. Therefore, the local / center server can archive data by extracting feature vectors for data by road section / traffic scenario, etc., and classifying / organizing the received data based on the extracted feature vectors. In this case, the local / center server can receive information regarding the current status of the terminal / surrounding road conditions, augment data of a similar road environment based on the received information, and provide the augmented road environment data (or augmented data) to the terminal.and / or, the local / center server may directly analyze the collected / received information regarding the status of the terminal / surrounding road conditions and data of the augmented road environment, and provide result data regarding the analysis results to the terminal. These operations and procedures may be applied identically between terminals or between terminals as the performance / capacity of the terminal evolves in the future.

[0271] However, when implementing such embodiments in the future, methods may be required to address issues regarding the safety and reliability of data / information that does not include certificates. For example, a dedicated authentication system for virtual data may be additionally required.

[0272] In the following, based on the content proposed in the section "Method and Device for Augmenting V2X Data and Multilayer Traffic Data" described above, a method for a first device and a terminal to augment road environment data, which is traffic data, and to perform predicted travel path and / or accident modeling using the augmented road environment data is described in detail.

[0273] FIG. 17 is a diagram illustrating a method for a first device to request a terminal to perform a data augmentation-based path prediction.

[0274] As described above, the first device may be an infrastructure, RSU, local server, network, or cloud for providing V2N services to multiple terminals. For example, the first device may collect road environment data (or traffic data) for multiple road sections in a predetermined geographical area based on terminal messages, sensors, cameras, map information, etc. As described above, the first device may provide the road environment data to the terminal to perform accident modeling for each road section using the road environment data, or to predict the predicted movement path of the terminal.

[0275] As described above, the terminal can predict a predicted movement path based on its movement status information (speed, angular velocity, acceleration, position, heading direction, etc.) and / or the road environment data. However, if the reliability / accuracy of the movement status information acquired by the terminal deteriorates, the communication quality associated with the terminal deteriorates, and / or there is a lack of road environment data available for the terminal to use, the prediction accuracy of the predicted movement path at the terminal may significantly decrease. It may be considerably difficult for the terminal itself to directly detect such a situation of deterioration in prediction accuracy. Therefore, the first device needs to predict / determine the deterioration of the terminal's prediction performance based on the road section where the terminal is located, the movement status information provided by the terminal, and the predicted movement path, and when the deterioration of the terminal's prediction performance is predicted / detected, it needs to trigger the operation of data augmentation-based path prediction at the terminal to improve the terminal's prediction accuracy.

[0276] Specifically, the first device can collect road environment data for at least one road section (S171). The road environment data may include at least one of accident occurrence location data, road structure data, traffic flow data, accident terminal trajectory data, accident cause data, accident type data, and weather data associated with each of the plurality of road sections. For example, as shown in Table 5, the road environment data may include vehicle data, traffic data, geometric structure data, environmental data, and cognitive / communication performance data. Alternatively, the first device may, as described above, use VAE or GAN to pre-enhance the road environment data for at least one specific road section where road environment data is sparse among the plurality of road sections, and train a prediction model for accident modeling for the at least one specific road section using the augmented road environment data (or augmented data). Alternatively, the first device may store the collected road environment data in a database based on a specific vector or provide it to terminals, as described in "7. Data Feature Vector Archiving".

[0277] Next, the first device may receive a first message from the terminal including information on the terminal's movement status and a first predicted movement path (S173). As described above, the information on the terminal's movement status may include mobility information such as the terminal's location, movement speed, acceleration, and heading direction.

[0278] Next, the first device may transmit a second message to the terminal requesting data augmentation-based path prediction based on the first road environment data for the first road section related to the terminal among the road environment data and the first message (S175). For example, the first device may determine whether the path prediction performance of the terminal is degraded based on the first road environment data for the first road section and the first message. Based on whether the path prediction performance of the terminal is degraded or the degree of degradation, the first device may determine the necessity of performing a data augmentation-based path prediction operation of the terminal. When it is determined that data augmentation-based path prediction is necessary at the terminal (e.g., when at least one of the trigger conditions defined in Table 6 above regarding augmented data is satisfied), the first device may transmit a second message to the terminal to trigger the data augmentation-based path prediction operation.

[0279] For example, the first device may transmit the second message to the terminal based on trigger conditions defined in Table 6. For example, if the amount of data of the first road environment data is less than a specific threshold amount, the first device may determine that the calculation / prediction performance of the predicted movement path at the terminal will deteriorate and transmit the second message requesting the data augmentation-based path prediction to the terminal. Alternatively, the first device may directly calculate the second predicted movement path for the terminal based on at least one of the first message, the first road environment data, and the augmented data obtained by augmenting the first road environment data, and if the difference / error between the first predicted movement path and the second predicted movement path is greater than or equal to a specific threshold error, it may determine that the calculation / prediction performance of the predicted movement path at the terminal will deteriorate and transmit the second message to the terminal. Alternatively, the first device may determine that the calculation / prediction performance of the predicted movement path at the terminal will deteriorate if the communication quality related to the first road section is less than a specific threshold quality and transmit the second message to the terminal. Alternatively, the first device may transmit the second message to the terminal when it is predicted that the terminal will enter a section where the movement paths of a plurality of terminals intersect or merge, by determining that the calculation / prediction performance of the predicted movement path at the terminal will be degraded.

[0280] Alternatively, the first device may provide the second message to the terminal, which further includes maneuver control information for controlling the driving of a vehicle associated with the terminal, when the accuracy of the terminal's movement path prediction is predicted to be low. Here, the first device may determine an intervention level related to maneuver control as described above and provide maneuver control information defined for each intervention level to the terminal through the second message or a separate message.

[0281] Data augmentation-based path prediction may be a method for predicting the predicted movement path by additionally considering augmented data obtained by augmenting the first road environment data as described above. Here, the augmented data may be data obtained by augmenting the first road environment data using VAE or GAN by the terminal or the first device. For example, the first device may provide the augmented data obtained by augmenting the first road environment data together with the second message to the terminal, or the terminal may directly augment the first road environment data to obtain the augmented data. Meanwhile, when the first device provides the augmented data to the terminal through the second message, the augmented data may be provided to the terminal with a method to which personal privacy issues can be mitigated, as explained in '6. Measures for Protecting Personal Privacy' above.

[0282] Here, the augmented data may be virtual road environment data generated by transforming or expanding the original road environment data through an artificial neural network-based data augmentation model so as to reflect various dynamic characteristics and driving situations related to the road section. For example, as described above, the augmented data may be generated using a deep learning-based model such as a VAE (Variational Autoencoder) or GAN (Generative Adversarial Network), thereby reflecting various driving conditions and interaction scenarios that are difficult to obtain using only actual observation data. For example, the first device may generate augmented data in which the speed, acceleration, yaw, braking pattern, road congestion, vehicle density, communication signal delay, etc. of surrounding terminals or vehicles in the road environment data are variously adjusted or modified using a deep learning-based model. The first device may generate augmented data that reflects various abnormal situations or sudden variables (sudden changes in weather conditions, appearance of obstacles, occurrence of construction zones) that may occur during driving using a deep learning-based model. By predicting / calculating the predicted movement path of the terminal based on such augmented data, the predicted movement path of the terminal can be predicted / calculated while considering various interactions with the surrounding environment of the terminal. In this case, the first device / terminal can calculate / predict a predicted movement path that is robust against changes in various environmental factors and highly accurate through the calculation / prediction of a predicted movement path that additionally reflects the aforementioned augmented data.

[0283] Figure 18 is a diagram illustrating how a terminal performs data augmentation-based path prediction.

[0284] A terminal may receive V2N services from a first device, which is an infrastructure, RSU, local server, network, or cloud, to multiple terminals. For example, a terminal may receive road environment data (or traffic data) regarding a road section related to itself from the first device. In this case, the terminal may calculate / predict the predicted movement path of the terminal using the collected movement status information of the terminal (speed, angular velocity, acceleration, position, heading direction, etc.) and / or the road environment data. Alternatively, the terminal may perform accident modeling for each road section using the road environment data, or train a prediction model for accident modeling (e.g., a prediction model based on an artificial neural network that performs accident modeling, such as accident frequency, accident severity, and accident probability). Alternatively, the terminal may receive parameters related to the prediction model from the first device without directly performing such accident modeling and training of the prediction model.

[0285] Referring to FIG. 18, the terminal can calculate / predict a first predicted movement path based on relevant road environment data (S181). For example, the terminal can calculate the predicted movement path of the terminal up to a predetermined future point in time based on the road environment data and the movement state information of the terminal. At this time, the terminal can calculate / predict the predicted movement path using a path prediction model (e.g., a path prediction model based on VAE) that is trained to calculate / predict the predicted movement path using the road environment information and / or the movement state information.

[0286] Next, the terminal may transmit a first message to the first device, the message including the terminal's movement state information and a first predicted movement path (S183). As described above, the terminal's movement state information may include mobility information such as the terminal's location, movement speed, acceleration, and heading direction.

[0287] Next, if the second message received from the first device includes information requesting a path prediction based on data augmentation, the terminal can predict a second predicted travel path by further considering the augmented data that augments the road environment data (S185). As described above, the second message may include the augmented data that the first device has augmented with the road environment data. Alternatively, the terminal may augment the road environment data using VAE or CAN. For example, the terminal may recognize that it is in a situation where the prediction performance of its predicted travel path is degraded based on the second message, and may obtain augmented data for the road environment data by performing augmentation on the road environment data to improve the prediction performance.

[0288] Specifically, the terminal (or the first device) can generate / acquire augmented data by augmenting the road environment data using an augmentation model (VAE or GAN) based on an artificial neural network, so as to reflect various driving conditions and interaction scenarios of surrounding vehicles that are difficult to obtain using only actual road environment data. For example, the first device or terminal can acquire / generate the augmented data by modifying / adjusting various environmental factors such as road congestion, vehicle density, and communication signal delay, as well as physical characteristics such as speed, acceleration, yaw, braking patterns, and lane change frequency of surrounding terminals or vehicles traveling on the road section. In this case, the terminal can predict its predicted travel path more precisely and flexibly even in complex driving scenarios by calculating / predicting its predicted travel path using the augmented data additionally. Furthermore, the terminal can significantly improve the accuracy of its predicted travel path by predicting the travel path using augmented data configured to simulate various abnormal situations or unexpected variables that may occur during driving.

[0289] According to such a configuration, the terminal does not always perform augmentation-based movement path prediction, but may selectively perform it only when necessary in accordance with instructions from a first device (e.g., a network). For example, the terminal may be operated in such a way that it provides mobility information to the first device, and the first device, upon receiving this information, determines the possibility of a decrease in the accuracy of the terminal's current or predicted movement path and then instructs the terminal on whether to perform augmentation-based prediction.

[0290] Thus, the proposed invention is configured such that a data augmentation-based path prediction operation at a terminal is triggered by the first device, thereby enabling efficient resource utilization and effective reduction of battery consumption at the terminal, and ensuring improved prediction quality and stability based on the first device. For example, since data augmentation-based path prediction is a computationally intensive process using neural networks such as VAE and GAN, continuous execution at the terminal may result in excessive computational resource and battery consumption. Therefore, by the first device, which is a network, controlling whether to perform a data augmentation-based path prediction operation at the terminal based on the road environment and / or the condition of the terminal, unnecessary computation at the terminal can be prevented, resource usage can be optimized, and battery consumption can be effectively reduced.

[0291] Furthermore, since the first device can comprehensively consider mobility information, traffic flow, and road conditions collected from multiple terminals, it can easily detect the possibility of a specific terminal's prediction performance degradation in a broader context. Consequently, it can preemptively induce data-augmented path prediction operations even in situations that the terminal is unaware of (e.g., changes in road congestion, abnormal patterns of nearby terminals, etc.). This can contribute to improving prediction stability and accuracy at the overall system level. For instance, since the first device has access to much richer external data (e.g., traffic sensors, map information, weather APIs, etc.) than the terminal, it can accurately determine whether the terminal's prediction performance is degraded by reflecting various driving environment conditions. Through this, it can induce augmented-based prediction execution based on accurate judgment criteria even in complex situations where the terminal itself finds it difficult to make a judgment. This can contribute to improving the prediction efficiency and adaptability of the entire system.

[0292] As such, the proposed invention effectively minimizes unnecessary computational resource usage and battery consumption in the terminal by ensuring that a data augmentation-based path prediction operation is performed in the terminal only when the terminal's prediction performance deteriorates. Furthermore, the proposed invention enables precise prediction to be performed at a more appropriate time by triggering a data augmentation-based path prediction operation on the terminal side, based on a determination made by the network side—which can comprehensively analyze mobility information, road environment information, and external data collected from various terminals—regarding whether the terminal's prediction performance has deteriorated. Through this, the appropriateness of prediction timing can be ensured and prediction accuracy can be improved. Moreover, by individually controlling whether to perform a data augmentation-based prediction operation according to the terminal's situation or performance level, the proposed invention can mitigate deviations in the quality of movement path prediction between terminals and improve the consistency and reliability of services provided by the entire system.

[0293] Example of a communication system to which the invention is applied

[0294] Although not limited thereto, the various descriptions, functions, procedures, proposals, methods, and / or flowcharts of the invention disclosed in this document may be applied to various fields requiring wireless communication / connection (e.g., 5G) between devices.

[0295] Examples are provided in more detail below with reference to the drawings. In the following drawings and descriptions, the same reference numerals may represent the same or corresponding hardware blocks, software blocks, or function blocks unless otherwise described.

[0296] FIG. 19 illustrates a communication system to which the present invention is applied.

[0297] Referring to FIG. 19, the communication system (1) to which the present invention applies includes a wireless device, a base station, and a network. Here, the wireless device refers to a device that performs communication using wireless access technology (e.g., 5G NR (New RAT), LTE (Long Term Evolution)) and may be referred to as a communication / wireless / 5G device. Although not limited thereto, the wireless device may include a robot (100a), a vehicle (100b-1, 100b-2), an XR (eXtended Reality) device (100c), a hand-held device (100d), a home appliance (100e), an IoT (Internet of Thing) device (100f), and an AI device / server (400). For example, the vehicle may include a vehicle equipped with wireless communication functions, an autonomous vehicle, a vehicle capable of performing inter-vehicle communication, etc. Here, the vehicle may include an Unmanned Aerial Vehicle (UAV) (e.g., a drone). XR devices include AR (Augmented Reality) / VR (Virtual Reality) / MR (Mixed Reality) devices and can be implemented in the form of HMDs (Head-Mounted Devices), HUDs (Head-Up Displays) equipped in vehicles, televisions, smartphones, computers, wearable devices, home appliances, digital signage, vehicles, robots, etc. Portable devices may include smartphones, smartpads, wearable devices (e.g., smartwatches, smart glasses), computers (e.g., laptops, etc.). Home appliances may include TVs, refrigerators, washing machines, etc. IoT devices may include sensors, smart meters, etc. For example, base stations and networks may be implemented as wireless devices, and a specific wireless device (200a) may operate as a base station / network node to other wireless devices.

[0298] Wireless devices (100a to 100f) can be connected to a network (300) through a base station (200). Artificial Intelligence (AI) technology may be applied to the wireless devices (100a to 100f), and the wireless devices (100a to 100f) can be connected to an AI server (400) through the network (300). The network (300) can be configured using a 3G network, a 4G (e.g., LTE) network, or a 5G (e.g., NR) network. The wireless devices (100a to 100f) may communicate with each other through the base station (200) / network (300), but they may also communicate directly (e.g., sidelink communication) without going through the base station / network. For example, vehicles (100b-1, 100b-2) can communicate directly (e.g., V2V (Vehicle to Vehicle) / V2X (Vehicle to everything) communication). Also, IoT devices (e.g., sensors) can communicate directly with other IoT devices (e.g., sensors) or other wireless devices (100a to 100f).

[0299] Wireless communication / connection (150a, 150b, 150c) can be established between wireless devices (100a~100f) / base station (200) and base station (200) / base station (200). Here, wireless communication / connection can be achieved through various wireless access technologies (e.g., 5G NR), such as uplink / downlink communication (150a), sidelink communication (150b) (or D2D communication), and inter-base station communication (150c) (e.g., relay, IAB (Integrated Access Backhaul)). Through wireless communication / connection (150a, 150b, 150c), wireless devices and base stations / wireless devices, and base stations and base stations can transmit / receive wireless signals to / from each other. For example, wireless communication / connection (150a, 150b, 150c) can transmit / receive signals through various physical channels. To this end, based on various proposals of the present invention, at least some of the following may be performed: various configuration information setting processes for transmitting / receiving wireless signals, various signal processing processes (e.g., channel encoding / decoding, modulation / demodulation, resource mapping / demapping, etc.), resource allocation processes, etc.

[0300] Example of a wireless device to which the present invention is applied

[0301] FIG. 20 illustrates a wireless device that can be applied to the present invention.

[0302] Referring to FIG. 20, the first wireless device (100) and the second wireless device (200) can transmit and receive wireless signals through various wireless access technologies (e.g., LTE, NR). Here, {the first wireless device (100), the second wireless device (200)} may correspond to {wireless device (100x), base station (200)} and / or {wireless device (100x), wireless device (100x)} of FIG. 19.

[0303] The first wireless device (100) includes one or more processors (102) and one or more memories (104), and may additionally include one or more transceivers (106) and / or one or more antennas (108). The processor (102) controls the memory (104) and / or transceivers (106) and may be configured to implement the descriptions, functions, procedures, proposals, methods and / or sequences of operation disclosed in this document. For example, the processor (102) may process information within the memory (104) to generate a first information / signal and then transmit a wireless signal containing the first information / signal through the transceiver (106). Additionally, the processor (102) may receive a wireless signal containing a second information / signal through the transceiver (106) and then store information obtained from the signal processing of the second information / signal in the memory (104). The memory (104) may be connected to the processor (102) and may store various information related to the operation of the processor (102). For example, the memory (104) may store software code containing instructions for performing some or all of the processes controlled by the processor (102) or for performing the descriptions, functions, procedures, proposals, methods, and / or operation sequence diagrams disclosed in this document. Here, the processor (102) and the memory (104) may be part of a communication modem / circuit / chipset designed to implement wireless communication technology (e.g., LTE, NR). The transceiver (106) may be connected to the processor (102) and may transmit and / or receive wireless signals through one or more antennas (108). The transceiver (106) may include a transmitter and / or receiver. The transceiver (106) may be combined with an RF (Radio Frequency) unit. In the present invention, the wireless device may refer to a communication modem / circuit / chipset.

[0304] The first wireless device or the first device (100) may include a processor (102), a memory (104), and a transceiver (106). The memory (104) may include at least one program capable of performing operations related to the embodiments described in FIGS. 16 to 18. The operations may include collecting road environment data for at least one road section, receiving a first message from a terminal including movement status information and a first predicted movement path, and transmitting a second message to the terminal requesting a data augmentation-based path prediction based on at least one of the first road environment data for the first road section related to the terminal and the first message.

[0305] Alternatively, a processing device may be configured including at least one processor (102) and a memory (104) for controlling a network. In this case, the processing device may include at least one processor; and at least one memory connected to the at least one processor and storing instructions that perform operations when executed by the at least one processor. The operations may include collecting road environment data for at least one road section, receiving a first message from a terminal including movement status information and a first predicted movement path, and transmitting a second message to the terminal requesting a data augmentation-based path prediction based on at least one of the first road environment data for the first road section related to the terminal and the first message. Additionally, at least one non-transient computer-readable medium may be configured including instructions that perform the operations.

[0306] The second wireless device (200) includes one or more processors (202) and one or more memories (204), and may additionally include one or more transceivers (206) and / or one or more antennas (208). The processor (202) controls the memory (204) and / or transceivers (206) and may be configured to implement the descriptions, functions, procedures, proposals, methods and / or operation sequences disclosed in this document. For example, the processor (202) may process information within the memory (204) to generate a third information / signal and then transmit a wireless signal containing the third information / signal through the transceiver (206). Additionally, the processor (202) may receive a wireless signal containing a fourth information / signal through the transceiver (206) and then store information obtained from the signal processing of the fourth information / signal in the memory (204). The memory (204) may be connected to the processor (202) and may store various information related to the operation of the processor (202). For example, the memory (204) may store software code containing instructions for performing some or all of the processes controlled by the processor (202) or for performing the descriptions, functions, procedures, proposals, methods, and / or operation sequence diagrams disclosed in this document. Here, the processor (202) and the memory (204) may be part of a communication modem / circuit / chip designed to implement wireless communication technology (e.g., LTE, NR). The transceiver (206) may be connected to the processor (202) and may transmit and / or receive wireless signals through one or more antennas (208). The transceiver (206) may include a transmitter and / or receiver. The transceiver (206) may be interchangeable with an RF unit. In the present invention, the wireless device may refer to a communication modem / circuit / chip.

[0307] The second wireless device or terminal (200) may include a transceiver (206), a processor (202), and a memory (204). The memory (204) may include at least one program capable of performing operations related to the embodiments described in FIGS. 16 to 18. The operations may include calculating a first predicted movement path for the terminal based on road environment data, transmitting a first message including the first predicted movement path and movement status information of the terminal to a first device, and, based on receiving a second message from the first device that includes information requesting a path prediction based on data augmentation, calculating a second predicted movement path using augmented data for the road environment data and the road environment data.

[0308] Hereinafter, hardware elements of the wireless device (100, 200) will be described in more detail. Although not limited thereto, one or more protocol layers may be implemented by one or more processors (102, 202). For example, one or more processors (102, 202) may implement one or more layers (e.g., functional layers such as PHY, MAC, RLC, PDCP, RRC, SDAP). One or more processors (102, 202) may generate one or more Protocol Data Units (PDUs) and / or Service Data Units (SDUs) according to the descriptions, functions, procedures, proposals, methods, and / or flowcharts of operation disclosed in this document. One or more processors (102, 202) may generate messages, control information, data, or information according to the descriptions, functions, procedures, proposals, methods, and / or flowcharts of operation disclosed in this document. One or more processors (102, 202) may generate a signal (e.g., baseband signal) containing a PDU, SDU, message, control information, data, or information according to the functions, procedures, proposals, and / or methods disclosed in this document and provide it to one or more transceivers (106, 206). One or more processors (102, 202) may receive a signal (e.g., baseband signal) from one or more transceivers (106, 206) and may obtain a PDU, SDU, message, control information, data, or information according to the descriptions, functions, procedures, proposals, methods, and / or flowcharts disclosed in this document.

[0309] One or more processors (102, 202) may be referred to as a controller, microcontroller, microprocessor, or microcomputer. One or more processors (102, 202) may be implemented by hardware, firmware, software, or a combination thereof. For example, one or more Application Specific Integrated Circuits (ASICs), one or more Digital Signal Processors (DSPs), one or more Digital Signal Processing Devices (DSPDs), one or more Programmable Logic Devices (PLDs), or one or more Field Programmable Gate Arrays (FPGAs) may be included in one or more processors (102, 202). The descriptions, functions, procedures, proposals, methods, and / or flowcharts disclosed in this document may be implemented using firmware or software, and the firmware or software may be implemented to include modules, procedures, functions, etc. Firmware or software configured to perform the descriptions, functions, procedures, proposals, methods, and / or operation sequences disclosed in this document may be contained in one or more processors (102, 202) or stored in one or more memories (104, 204) and driven by one or more processors (102, 202). The descriptions, functions, procedures, proposals, methods, and / or operation sequences disclosed in this document may be implemented using firmware or software in the form of code, instructions, and / or sets of instructions.

[0310] One or more memories (104, 204) may be connected to one or more processors (102, 202) and may store various forms of data, signals, messages, information, programs, code, instructions, and / or commands. One or more memories (104, 204) may be composed of ROM, RAM, EPROM, flash memory, hard drive, registers, cache memory, computer read storage media, and / or combinations thereof. One or more memories (104, 204) may be located inside and / or outside of one or more processors (102, 202). Additionally, one or more memories (104, 204) may be connected to one or more processors (102, 202) through various technologies such as wired or wireless connections.

[0311] One or more transceivers (106, 206) may transmit user data, control information, wireless signals / channels, etc., as mentioned in the methods and / or operation flowcharts, etc., of this document to one or more other devices. One or more transceivers (106, 206) may receive user data, control information, wireless signals / channels, etc., as mentioned in the descriptions, functions, procedures, proposals, methods and / or operation flowcharts, etc., disclosed in this document from one or more other devices. For example, one or more transceivers (106, 206) may be connected to one or more processors (102, 202) and may transmit and receive wireless signals. For example, one or more processors (102, 202) may control one or more transceivers (106, 206) to transmit user data, control information, or wireless signals to one or more other devices. Additionally, one or more processors (102, 202) may control one or more transceivers (106, 206) to receive user data, control information, or wireless signals from one or more other devices. Additionally, one or more transceivers (106, 206) may be connected to one or more antennas (108, 208), and one or more transceivers (106, 206) may be configured to transmit and receive user data, control information, wireless signals / channels, etc., as described in the descriptions, functions, procedures, proposals, methods, and / or flowcharts of operation disclosed in this document through one or more antennas (108, 208). In this document, one or more antennas may be multiple physical antennas or multiple logical antennas (e.g., antenna ports). One or more transceivers (106, 206) can convert the received wireless signal / channel, etc. from an RF band signal to a baseband signal in order to process the received user data, control information, wireless signal / channel, etc. using one or more processors (102, 202).One or more transceivers (106, 206) can convert user data, control information, wireless signals / channels, etc. processed using one or more processors (102, 202) from baseband signals to RF band signals. To this end, one or more transceivers (106, 206) may include (analog) oscillators and / or filters.

[0312] Examples of wireless device applications to which the present invention is applied

[0313] FIG. 21 illustrates another example of a wireless device to which the present invention applies. The wireless device may be implemented in various forms depending on the use-example / service (see FIG. 19).

[0314] Referring to FIG. 21, the wireless device (100, 200) corresponds to the wireless device (100, 200) of FIG. 20 and may be composed of various elements, components, units / parts, and / or modules. For example, the wireless device (100, 200) may include a communication unit (110), a control unit (120), a memory unit (130), and additional elements (140). The communication unit may include a communication circuit (112) and transceiver(s) (114). For example, the communication circuit (112) may include one or more processors (102, 202) and / or one or more memories (104, 204) of FIG. 21. For example, the transceiver(s) (114) may include one or more transceivers (106, 206) and / or one or more antennas (108, 208) of FIG. 20. The control unit (120) is electrically connected to the communication unit (110), the memory unit (130), and additional elements (140) and controls the general operation of the wireless device. For example, the control unit (120) may control the electrical / mechanical operation of the wireless device based on a program / code / command / information stored in the memory unit (130). Additionally, the control unit (120) may transmit information stored in the memory unit (130) to an external (e.g., another communication device) via a wireless / wired interface through the communication unit (110), or store information received from an external (e.g., another communication device) via a wireless / wired interface through the communication unit (110) in the memory unit (130).

[0315] The additional element (140) can be configured in various ways depending on the type of wireless device. For example, the additional element (140) may include at least one of a power unit / battery, an input / output unit (I / O unit), a driving unit, and a computing unit. Although not limited thereto, the wireless device may be implemented in the form of a robot (Fig. 19, 100a), a vehicle (Fig. 19, 100b-1, 100b-2), an XR device (Fig. 19, 100c), a portable device (Fig. 19, 100d), a home appliance (Fig. 19, 100e), an IoT device (Fig. 19, 100f), a digital broadcasting terminal, a hologram device, a public safety device, an MTC device, a medical device, a fintech device (or financial device), a security device, a climate / environment device, an AI server / device (Fig. 19, 400), a base station (Fig. 19, 200), a network node, etc. Wireless devices can be used in a movable or fixed location depending on the use—e.g., service.

[0316] In FIG. 21, various elements, components, units / parts, and / or modules within the wireless device (100, 200) may be entirely interconnected via a wired interface, or at least partially connected via a communication unit (110). For example, within the wireless device (100, 200), the control unit (120) and the communication unit (110) may be connected via a wire, and the control unit (120) and the first unit (e.g., 130, 140) may be connected wirelessly via the communication unit (110). Additionally, each element, component, unit / part, and / or module within the wireless device (100, 200) may include one or more additional elements. For example, the control unit (120) may be composed of one or more sets of processors. For example, the control unit (120) may be composed of a set of a communication control processor, an application processor, an Electronic Control Unit (ECU), a graphics processing processor, a memory control processor, etc. As another example, the memory unit (130) may be composed of RAM (Random Access Memory), DRAM (Dynamic RAM), ROM (Read Only Memory), flash memory, volatile memory, non-volatile memory and / or a combination thereof.

[0317] Examples of vehicles or autonomous vehicles to which the present invention is applied

[0318] FIG. 22 illustrates a vehicle or autonomous vehicle to which the present invention applies. The vehicle or autonomous vehicle may be implemented as a mobile robot, a vehicle, a train, an aerial vehicle (AV), a ship, etc.

[0319] Referring to FIG. 22, a vehicle or autonomous vehicle (100) may include an antenna unit (108), a communication unit (110), a control unit (120), a driving unit (140a), a power supply unit (140b), a sensor unit (140c), and an autonomous driving unit (140d). The antenna unit (108) may be configured as part of the communication unit (110). Blocks 110 / 130 / 140a to 140d each correspond to blocks 110 / 130 / 140 of FIG. 21.

[0320] The communication unit (110) can transmit and receive signals (e.g., data, control signals, etc.) with external devices such as other vehicles, base stations (e.g., base stations, roadside base stations (Roadside units), etc.), and servers. The control unit (120) can perform various operations by controlling elements of the vehicle or autonomous vehicle (100). The control unit (120) may include an Electronic Control Unit (ECU). The driving unit (140a) can drive the vehicle or autonomous vehicle (100) on the ground. The driving unit (140a) may include an engine, motor, power train, wheels, brakes, steering device, etc. The power supply unit (140b) supplies power to the vehicle or autonomous vehicle (100) and may include wired / wireless charging circuits, batteries, etc. The sensor unit (140c) can obtain vehicle status, surrounding environment information, user information, etc. The sensor unit (140c) may include an IMU (inertial measurement unit) sensor, a collision sensor, a wheel sensor, a speed sensor, an inclination sensor, a weight detection sensor, a heading sensor, a position module, a vehicle forward / reverse sensor, a battery sensor, a fuel sensor, a tire sensor, a steering sensor, a temperature sensor, a humidity sensor, an ultrasonic sensor, an illuminance sensor, a pedal position sensor, etc. The autonomous driving unit (140d) may implement technologies such as maintaining the driving lane, technologies for automatically adjusting speed such as adaptive cruise control, technologies for automatically driving along a predetermined path, and technologies for automatically setting a path and driving when a destination is set.

[0321] For example, the communication unit (110) can receive map data, traffic information data, etc. from an external server. The autonomous driving unit (140d) can generate an autonomous driving path and a driving plan based on the acquired data. The control unit (120) can control the drive unit (140a) so that the vehicle or the autonomous vehicle (100) moves along the autonomous driving path according to the driving plan (e.g., speed / direction control). During autonomous driving, the communication unit (110) can acquire the latest traffic information data from an external server non-periodically and can acquire surrounding traffic information data from surrounding vehicles. Additionally, during autonomous driving, the sensor unit (140c) can acquire vehicle status and surrounding environment information. The autonomous driving unit (140d) can update the autonomous driving path and the driving plan based on the newly acquired data / information. The communication unit (110) can transmit information regarding the vehicle location, autonomous driving path, driving plan, etc. to an external server. An external server can predict traffic information data in advance using AI technology, etc., based on information collected from vehicles or autonomous vehicles, and can provide the predicted traffic information data to vehicles or autonomous vehicles.

[0322] The wireless communication technology implemented in the wireless device (XXX, YYY) of this specification may include LTE, NR, and 6G, as well as Narrowband Internet of Things for low-power communication. In this case, for example, NB-IoT technology may be an example of LPWAN (Low Power Wide Area Network) technology and may be implemented according to standards such as LTE Cat NB1 and / or LTE Cat NB2, but is not limited to the names mentioned above. Additionally, or generally, the wireless communication technology implemented in the wireless device (XXX, YYY) of this specification may perform communication based on LTE-M technology. In this case, for example, LTE-M technology may be an example of LPWAN technology and may be referred to by various names such as eMTC (enhanced Machine Type Communication). For example, LTE-M technology may be implemented in at least one of various standards such as 1) LTE CAT 0, 2) LTE Cat M1, 3) LTE Cat M2, 4) LTE non-BL (non-Bandwidth Limited), 5) LTE-MTC, 6) LTE Machine Type Communication, and / or 7) LTE M, and is not limited to the names mentioned above. Additionally or generally, wireless communication technology implemented in the wireless device (XXX, YYY) of this specification may include at least one of ZigBee, Bluetooth, and Low Power Wide Area Network (LPWAN) with consideration for low-power communication, and is not limited to the names mentioned above. As an example, ZigBee technology can create personal area networks (PANs) related to small / low-power digital communication based on various standards such as IEEE 802.15.4, and may be referred to by various names.

[0323] The embodiments described above are combinations of the components and features of the present invention in a specific form. Each component or feature should be considered optional unless otherwise explicitly stated. Each component or feature may be implemented in a form not combined with other components or features. Additionally, it is possible to construct embodiments of the present invention by combining some components and / or features. The order of operations described in the embodiments of the present invention may be changed. Some components or features of one embodiment may be included in another embodiment, or may be replaced with corresponding components or features of another embodiment. It is obvious that embodiments may be constructed by combining claims that do not have an explicit citation relationship in the claims, or that new claims may be included by amendment after filing.

[0324] In this document, embodiments of the present invention are described primarily with a focus on the signal transmission and reception relationship between a terminal and a base station. This transmission and reception relationship is extended in the same or similar manner to signal transmission and reception between a terminal and a relay or between a base station and a relay. Specific operations described in this document as being performed by a base station may, in some cases, be performed by an upper node. That is, it is self-evident that various operations performed for communication with a terminal in a network consisting of multiple network nodes including a base station may be performed by the base station or other network nodes other than the base station. The base station may be replaced by terms such as fixed station, Node B, eNode B (eNB), and access point. Additionally, the terminal may be replaced by terms such as User Equipment (UE), Mobile Station (MS), and Mobile Subscriber Station (MSS).

[0325] Embodiments according to the present invention may be implemented by various means, for example, hardware, firmware, software, or a combination thereof. In the case of implementation by hardware, one embodiment of the present invention may be implemented by one or more ASICs (application specific integrated circuits), DSPs (digital signal processors), DSPDs (digital signal processing devices), PLDs (programmable logic devices), FPGAs (field programmable gate arrays), processors, controllers, microcontrollers, microprocessors, etc.

[0326] In the case of implementation by firmware or software, an embodiment of the present invention may be implemented in the form of a module, procedure, function, etc., that performs the functions or operations described above. Software code may be stored in a memory unit and executed by a processor. The memory unit may be located inside or outside the processor and may exchange data with the processor by various means already known.

[0327] It is obvious to those skilled in the art that the present invention may be embodied in other specific forms without departing from the features of the invention. Accordingly, the foregoing detailed description should not be interpreted restrictively in all respects but should be considered exemplary. The scope of the invention shall be determined by a reasonable interpretation of the appended claims, and all modifications within the equivalent scope of the invention are included within the scope of the invention.

[0328] The embodiments of the present invention as described above can be applied to various mobile communication systems.

Claims

In the method using the first device, A step of collecting road environment data for at least one road section; A step of receiving a first message from a terminal including movement status information and a first predicted movement path; and A method comprising the step of transmitting a second message to the terminal requesting a data augmentation-based path prediction based on at least one of the first road environment data for a first road section related to the terminal among the road environment data and the first message. In paragraph 1, The above data augmentation-based path prediction is a method of predicting the predicted movement path based on augmented data obtained by augmenting the first road environment data using a VAE (variational autoencoder) or a GAN (generative adversarial network). In paragraph 1, A method in which the second message is transmitted to the terminal based on the fact that the amount of data of the first road environment data is less than a specific threshold amount. In paragraph 1, The first device calculates a second predicted movement path for the terminal based on at least one of the first message, the first road environment data, and augmented data augmented with the first road environment data, and A method in which the second message is transmitted to the terminal based on the difference between the first predicted movement path and the second predicted movement path being greater than a certain threshold. In paragraph 1, A method in which the second message is transmitted to the terminal based on the prediction that the terminal will enter a road section where the movement paths of a plurality of terminals intersect or merge. In paragraph 1, A method in which the second message is transmitted to the terminal based on the fact that the communication quality associated with the first road section is less than a specific threshold quality. In paragraph 1, A method comprising: the second message including augmented data obtained by augmenting the first road environment data using a VAE (variational autoencoder) or a GAN (generative adversarial network). In Paragraph 7, A method in which the validity of the above augmented data is analyzed based on an authentication system separately configured for virtual data. In paragraph 1, A method comprising at least one of accident occurrence location data, road structure data, traffic flow data, accident terminal trajectory data, accident cause data, accident type data, and weather data related to the first road section. In paragraph 1, A method in which the second message further includes maneuver control information for controlling the maneuver of the terminal. In at least one non-transient computer-readable recording medium, Includes instructions that perform operations when executed by at least one processor, The above operations are, Collect road environment data for at least one road section; Receiving a first message from a terminal including movement status information and a first predicted movement path; and At least one non-transient computer-readable recording medium comprising transmitting a second message to the terminal requesting a data augmentation-based path prediction based on at least one of the first road environment data for a first road section related to the terminal among the road environment data and the first message. In the first device, RF (Radio Frequency) transceiver; and It includes a processor connected to the above RF transceiver, and A first device, wherein the processor controls the RF transceiver to collect road environment data for at least one road section, receives a first message from a terminal including movement status information and a first predicted movement path, and transmits a second message to the terminal requesting a data augmentation-based path prediction based on at least one of the first road environment data for a first road section related to the terminal among the road environment data and the first message. In a processing device that controls a first device, At least one processor; and It includes at least one memory that stores instructions connected to the above at least one processor and performing operations when executed by the at least one processor, The above operations are, Collect road environment data for at least one road section; Receiving a first message from a terminal including movement status information and a first predicted movement path; and A processing device comprising transmitting a second message to the terminal requesting a data augmentation-based path prediction based on at least one of the first road environment data for a first road section related to the terminal among the road environment data and the first message. In a method using a terminal, A step of calculating a first predicted travel path based on road environment data; A step of transmitting a first message to a first device including the first predicted movement path and the movement status information of the terminal; and A method comprising the step of calculating a second predicted travel path using augmented data for the road environment data and the road environment data, based on receiving a second message containing information requesting a path prediction based on data augmentation from the first device. In the terminal, RF (Radio Frequency) transceiver; and It includes a processor connected to the above RF transceiver, and A terminal, wherein the processor controls the RF transceiver to calculate a first predicted movement path for the terminal based on road environment data, transmits a first message including the first predicted movement path and movement status information of the terminal to a first device, and calculates a second predicted movement path using augmented data for the road environment data and the road environment data based on the receipt of a second message from the first device which includes information requesting a path prediction based on data augmentation.

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